Configured artificial intelligence systems and methods for software-defined vehicles

Abstract

The present disclosure relates to configured artificial intelligence methods and systems and related transportation systems and methods, including software-defined vehicles, for transportation systems using sensor and other data, and the integration of a transportation system with an AI convergence system of systems, providing a multi-layered system for intelligent automation and data-driven decision making across operational aspects of a transportation system.

Description

SFT-108-A-PCT CONFIGURED ARTIFICIAL INTELLIGENCE SYSTEMS AND METHODS FOR SOFTWARE-DEFINED VEHICLES CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No.63 / 674,494, filed 23 July 2024.

[0002] This application also is a continuation-in-part of, and claims priority to U.S. Patent Application Serial No. 19 / 277,180, filed 22 July 2025, which is a continuation of International Application No. PCT / US2025 / 012973, filed 24 January 2025, which claims the benefit of priority to the following U.S. Patent Applications: Serial No.63 / 625,609, filed 26 January 2024; Serial No.63 / 638,591, filed 25 April 2024; and Serial No.63 / 639,912, filed 29 April 2024.

[0003] The patent applications referenced above are hereby incorporated by reference as if fully set forth herein in their entirety. TECHNICAL FIELD

[0004] The present disclosure relates to transportation and related methods and systems including the integration of a transportation system with an AI convergence system of systems, representing a multi-layered system for intelligent automation and data-driven decision making across operational aspects of a transportation system. BACKGROUND

[0005] Traditional enterprise operations in transportation often rely on separate, disconnected layers of technology infrastructure. Governance is largely manual, requiring significant human oversight to enforce policies, monitor compliance, and manage digital rights. Organizations struggle to maintain consistent oversight across different operational domains of a transportation system and often faced challenges in adapting to changing regulatory requirements.

[0006] Transportation systems typically operate with rigid, predefined offerings that lack the ability to dynamically adapt to user needs or environmental conditions. Such operations suffer from fragmented management of AI systems and technological resources. Organizations lack sufficient capabilities for AI system generation, training, verification, and deployment. The absence of coordinated operations modules means that AI systems were developed and deployed in isolation, without proper governance or optimization across the enterprise.

[0007] The lack of intelligent integration between different technological layers of a transportation system creates significant inefficiencies in operations. This fragmented approach limits the ability to leverage emerging technologies effectively and restricts the potential for innovation in transportation services delivery. These limitations in traditional transportation systems create a clear need for a more integrated, intelligent approach to technology infrastructure. SUMMARY

[0008] In some aspects, the techniques described herein relate to a computer-implemented system, the system including:

[0009] In some aspects, the techniques described herein relate to a configured artificial intelligence system for transportation including: a processor and memory configured to execute an intelligence system that provides intelligence service to an intelligence client in a transportation environment; an intelligence controller that acts as a control tower for the intelligence service and coordinates performance of a task; a model execution system that executes an artificial intelligence model for transportation decision-making; a data services system that processes transportation data; a security system that monitors the transportation data for adversarial attack; and a permissions system that controls access to the transportation intelligence service. Page 1 of 713SFT-108-A-PCT

[0010] In some aspects, the techniques described herein relate to a system, wherein the intelligence system includes a training and reinforcement system that maintains and trains the artificial intelligence model for transportation applications.

[0011] In some aspects, the techniques described herein relate to a system, wherein the intelligence system includes a governance and analysis system that analyzes the artificial intelligence model for transportation compliance.

[0012] In some aspects, the techniques described herein relate to a system, wherein the intelligence system includes a scoring system that generates a score for transportation data quality.

[0013] In some aspects, the techniques described herein relate to a system, wherein the intelligence system includes a model interface system that receives input indicating a transportation task.

[0014] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence model includes a large language model configured to process transportation-related text data.

[0015] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence model includes a multimodal model that processes transportation data across multiple format.

[0016] In some aspects, the techniques described herein relate to a system, wherein the security system includes a know your data system that monitors input data provided to the artificial intelligence model for adversarial attack by analyzing patterns in real-time transportation data stream.

[0017] In some aspects, the techniques described herein relate to a system, wherein the data services system processes sensor data from a transportation vehicle.

[0018] In some aspects, the techniques described herein relate to a system, wherein the intelligence controller delegates a subtask to the artificial intelligence model for transportation decision-making.

[0019] In some aspects, the techniques described herein relate to a system, wherein the permissions system controls access to transportation resources through the intelligence service.

[0020] In some aspects, the techniques described herein relate to a system, further including a reporting system that generates a compliance report for the intelligence service in the transportation environment.

[0021] In some aspects, the techniques described herein relate to a configured artificial intelligence system for software defined vehicle including: a processor and memory configured to execute a know your model system that manages a lifecycle of an artificial intelligence model deployed in a software defined vehicle; the know your model system configured to perform a model intake and registration action associated with a candidate artificial intelligence model for vehicle application; the know your model system configured to perform a model evaluation action associated with the candidate artificial intelligence model; the know your model system configured to perform a model deployment action that integrates the candidate artificial intelligence model into a vehicle inference platform; and a model execution system that executes the deployed artificial intelligence model for vehicle control decision.

[0022] In some aspects, the techniques described herein relate to a system, wherein the know your model system is configured to perform a model monitoring and observability action associated with the deployed artificial intelligence model.

[0023] In some aspects, the techniques described herein relate to a system, wherein the know your model system is configured to perform a model updating and retraining action associated with the deployed artificial intelligence model. Page 2 of 713SFT-108-A-PCT

[0024] In some aspects, the techniques described herein relate to a system, wherein the model intake and registration action includes validating that the candidate artificial intelligence model complies with dataset licensing requirement for vehicle application.

[0025] In some aspects, the techniques described herein relate to a system, wherein the model evaluation and risk assessment action includes determining regulatory alignment of the candidate artificial intelligence model with applicable regulatory organization.

[0026] In some aspects, the techniques described herein relate to a system, wherein the model deployment action includes production integration that hosts the deployed artificial intelligence model behind a standardized endpoint for vehicle system access.

[0027] In some aspects, the techniques described herein relate to a system, wherein the know your model system includes a distributed trust ledger component that immutably records onboarding step and operational event for the deployed artificial intelligence model.

[0028] In some aspects, the techniques described herein relate to a system, wherein the model evaluation and risk assessment action includes automated environment validation of the candidate artificial intelligence model prior to vehicle deployment.

[0029] In some aspects, the techniques described herein relate to a system, wherein the know your model system performs endpoint configuration testing on the candidate artificial intelligence model for vehicle integration.

[0030] In some aspects, the techniques described herein relate to a system, wherein the know your model system performs security and privacy validation on the candidate artificial intelligence model as part of the model intake and registration action.

[0031] In some aspects, the techniques described herein relate to a system, wherein the model deployment action includes automatic scaling of model inference based on demand from vehicle system client.

[0032] In some aspects, the techniques described herein relate to a configured artificial intelligence system for physical artificial intelligence in transportation including: a processor and memory configured to execute a know your physical artificial intelligence system that manages onboarding and deployment of a physical artificial intelligence system within a transportation environment; the know your physical artificial intelligence system configured to perform discovery and authentication action for the physical artificial intelligence system upon initial connection to a transportation network; the know your physical artificial intelligence system configured to receive and process a capability declaration from the physical artificial intelligence system that describes supported artificial intelligence task for transportation; the know your physical artificial intelligence system configured to execute policy and compliance validation action by comparing the capability declaration against organizational governance policy; and the know your physical artificial intelligence system configured to perform contextual configuration and operational parameterization of the physical artificial intelligence system following compliance approval.

[0033] In some aspects, the techniques described herein relate to a system, wherein the discovery and authentication action includes hardware attestation using trusted platform modules to verify device integrity of the physical artificial intelligence system.

[0034] In some aspects, the techniques described herein relate to a system, wherein the capability declaration includes a machine-readable manifest describing supported artificial intelligence task and hardware specification of the physical artificial intelligence system. Page 3 of 713SFT-108-A-PCT

[0035] In some aspects, the techniques described herein relate to a system, wherein the policy and compliance validation action utilizes a policy-as-code approach to assess compliance state of the physical artificial intelligence system.

[0036] In some aspects, the techniques described herein relate to a system, wherein the contextual configuration includes transmission of environment maps and dynamic scheduling parameter to the physical artificial intelligence system.

[0037] In some aspects, the techniques described herein relate to a system, wherein the know your physical artificial intelligence system validates calibration and performance of sensor against known baseline for the physical artificial intelligence system.

[0038] In some aspects, the techniques described herein relate to a system, wherein the know your physical artificial intelligence system provides secure firmware update to the physical artificial intelligence system.

[0039] In some aspects, the techniques described herein relate to a system, wherein the know your physical artificial intelligence system aggregates telemetry data from the physical artificial intelligence system for fleet performance monitoring.

[0040] In some aspects, the techniques described herein relate to a system, wherein the know your physical artificial intelligence system executes simulated trial involving the physical artificial intelligence system before real-world deployment.

[0041] In some aspects, the techniques described herein relate to a system, wherein the know your physical artificial intelligence system includes integration with a digital twin system that generates a digital twin representation of the physical artificial intelligence system.

[0042] In some aspects, the techniques described herein relate to a system, wherein the know your physical artificial intelligence system initiates a secure erasure process upon decommissioning of the physical artificial intelligence system.

[0043] In some aspects, the techniques described herein relate to a configured artificial intelligence system for transportation artificial intelligence agent including: a processor and memory configured to execute an intelligent agent system that provides an artificial intelligence agent for transportation task; the artificial intelligence agent including at least one artificial intelligence model configured to receive a user prompt related to transportation and to take action to fulfill the user prompt; the artificial intelligence agent configured to operate based on training that involves a training data set related to transportation and a training metric; the artificial intelligence agent entrusted with a set of resource including computational resource and data source for transportation decision-making; and an intelligence controller that observes, rationalizes, verifies, alters, and regulates the artificial intelligence agent to generate understanding of behavior of the artificial intelligence agent in transportation context.

[0044] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence agent is configured to control physical action and interaction of a transportation device.

[0045] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence agent operates autonomously with little supervision in a transportation environment.

[0046] In some aspects, the techniques described herein relate to a system, wherein the intelligence controller requires the artificial intelligence agent to adopt interpretable artificial intelligence model for transportation decision transparency. Page 4 of 713SFT-108-A-PCT

[0047] In some aspects, the techniques described herein relate to a system, wherein the intelligence controller requires the artificial intelligence agent to log and preserve internal reasoning as stepwise progression from input to output for transportation tasks.

[0048] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence agent is organized to support interpretability through modular design with bounded reasoning tasks for transportation application.

[0049] In some aspects, the techniques described herein relate to a system, wherein the intelligence controller generates a record that preserves and documents reasoning of the artificial intelligence agent for transportation decision.

[0050] In some aspects, the techniques described herein relate to a system, wherein the intelligence controller generates notification and alert in response to discovering a problem with the artificial intelligence agent in transportation context.

[0051] In some aspects, the techniques described herein relate to a system, wherein an intelligence controller intervenes in current processing of the artificial intelligence agent to prevent error in transportation decision-making.

[0052] In some aspects, the techniques described herein relate to a system, wherein the intelligence controller executes a randomized inspection and an obfuscated test of the artificial intelligence agent for transportation safety.

[0053] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence agent includes access to a transportation tool that performs action in a transportation system.

[0054] In some aspects, the techniques described herein relate to a method for managing an artificial intelligence model in a transportation system, the method including: executing a configured artificial intelligence system; receiving, by the intelligence system, a candidate artificial intelligence model for deployment in a software defined vehicle; performing, by a know your model system within the intelligence system, a model intake and registration action on the candidate artificial intelligence model; executing, by the know your model system, a model evaluation action on the candidate artificial intelligence model; performing, by the know your model system, a model deployment action to deploy the candidate artificial intelligence model in the software defined vehicle; and monitoring, by the know your model system, the deployed artificial intelligence model during operation of the software defined vehicle.

[0055] In some aspects, the techniques described herein relate to a method, wherein the model intake and registration action includes validating that the candidate artificial intelligence model complies with a dataset licensing requirement.

[0056] In some aspects, the techniques described herein relate to a method, wherein a model evaluation and risk assessment action includes performing endpoint configuration testing on the candidate artificial intelligence model.

[0057] In some aspects, the techniques described herein relate to a method, wherein a model evaluation and risk assessment action includes performing security and privacy validation on the candidate artificial intelligence model.

[0058] In some aspects, the techniques described herein relate to a method, wherein the know your model system includes a transformer-based large language model fine-tuned for legal document classification.

[0059] In some aspects, the techniques described herein relate to a method, wherein the model deployment action includes performing automated environment validation of the candidate artificial intelligence model prior to production deployment.

[0060] In some aspects, the techniques described herein relate to a method, wherein the monitoring includes performing model monitoring and observability action on the deployed artificial intelligence model.

[0061] In some aspects, the techniques described herein relate to a method, wherein the know your model system is configured to perform model updating and retraining action on the deployed artificial intelligence model. Page 5 of 713SFT-108-A-PCT

[0062] In some aspects, the techniques described herein relate to a method, wherein a digital twin system generates a digital twin representation of the candidate artificial intelligence model during the model intake and registration action.

[0063] In some aspects, the techniques described herein relate to a method, wherein a model evaluation and risk assessment action includes evaluating alignment and compliance of the candidate artificial intelligence model with a regulatory requirement.

[0064] In some aspects, the techniques described herein relate to a method, wherein the know your model system incorporates a distributed trust ledger component to record an onboarding step and a policy validation.

[0065] In some aspects, the techniques described herein relate to a method, further including performing, by the know your model system, a model decommissioning action on the deployed artificial intelligence model when the software defined vehicle is retired.

[0066] In some aspects, the techniques described herein relate to a method for authenticating a physical artificial intelligence system in a transportation environment, the method including: executing a configured artificial intelligence system including an intelligence system and a know your physical artificial intelligence system; receiving, by the know your physical artificial intelligence system, a discovery packet from a physical artificial intelligence device associated with a vehicle, wherein the discovery packet includes at least one of a cryptographic identity credential, a device serial number, and a pre-provisioned public key infrastructure certificate; performing, by the know your physical artificial intelligence system, hardware attestation using a trusted platform module to verify device authenticity of the physical artificial intelligence device; establishing, by the know your physical artificial intelligence system, an encrypted operational session with the physical artificial intelligence device; issuing, by the know your physical artificial intelligence system, an ephemeral operational token to the physical artificial intelligence device; and provisioning, by the know your physical artificial intelligence system, a signed session certificate that allows the physical artificial intelligence device to interact with a system component of the configured artificial intelligence system.

[0067] In some aspects, the techniques described herein relate to a method, wherein the discovery packet is broadcast via a secure networking protocol including mutual transport layer security.

[0068] In some aspects, the techniques described herein relate to a method, wherein the hardware attestation uses a secure enclave cryptographic signature to verify device integrity.

[0069] In some aspects, the techniques described herein relate to a method, wherein establishing the encrypted operational session includes using a zero-trust network onboarding standard.

[0070] In some aspects, the techniques described herein relate to a method, wherein the signed session certificate allows the physical artificial intelligence device to interact with an orchestration component of the configured artificial intelligence system.

[0071] In some aspects, the techniques described herein relate to a method, wherein the signed session certificate allows the physical artificial intelligence device to interact with a backend service of the configured artificial intelligence system.

[0072] In some aspects, the techniques described herein relate to a method, further including receiving, by the know your physical artificial intelligence system, a comprehensive capability declaration from the physical artificial intelligence device after authentication.

[0073] In some aspects, the techniques described herein relate to a method, wherein the cryptographic identity credential includes a pre-provisioned public key infrastructure certificate. Page 6 of 713SFT-108-A-PCT

[0074] In some aspects, the techniques described herein relate to a method, wherein the ephemeral operational token has a limited validity period for enhanced security.

[0075] In some aspects, the techniques described herein relate to a method, further including validating, by the know your physical artificial intelligence system, the device serial number against a registered device database.

[0076] In some aspects, the techniques described herein relate to a method, wherein the physical artificial intelligence device includes a mobile robot integrated into the vehicle.

[0077] In some aspects, the techniques described herein relate to a method, wherein the physical artificial intelligence device includes an autonomous surveillance unit integrated into the vehicle.

[0078] In some aspects, the techniques described herein relate to a method for validating capability of a physical artificial intelligence system in a vehicle, the method including: executing a configured artificial intelligence system including a know your physical artificial intelligence system; receiving, by the know your physical artificial intelligence system, a capability declaration from a physical artificial intelligence device integrated into a vehicle, wherein the capability declaration includes a machine-readable manifest describing at least one of a supported artificial intelligence task, a hardware specification, an operational constraint, and a certified artificial intelligence model signature; parsing, by the know your physical artificial intelligence system, the capability declaration against an enterprise-wide capability ontology; validating, by the know your physical artificial intelligence system, the capability declaration; storing, by the know your physical artificial intelligence system, the capability declaration within a centralized dynamic system registry; and performing, by the know your physical artificial intelligence system, a policy check based on the stored capability declaration.

[0079] In some aspects, the techniques described herein relate to a method, wherein the supported artificial intelligence task includes object detection.

[0080] In some aspects, the techniques described herein relate to a method, wherein the supported artificial intelligence task includes obstacle avoidance.

[0081] In some aspects, the techniques described herein relate to a method, wherein the supported artificial intelligence task includes semantic segmentation.

[0082] In some aspects, the techniques described herein relate to a method, wherein the hardware specification includes a light detection and ranging sensor model.

[0083] In some aspects, the techniques described herein relate to a method, wherein the hardware specification includes a manipulator degree of freedom specification.

[0084] In some aspects, the techniques described herein relate to a method, wherein the operational constraint includes a maximum operating temperature.

[0085] In some aspects, the techniques described herein relate to a method, wherein the operational constraint includes a permissible indoor and outdoor transition specification.

[0086] In some aspects, the techniques described herein relate to a method, wherein the certified artificial intelligence model signature includes a secure hash algorithm 256 hash of neural network weight.

[0087] In some aspects, the techniques described herein relate to a method, wherein the capability declaration further includes battery management detail.

[0088] In some aspects, the techniques described herein relate to a method, wherein the centralized dynamic system registry includes a graph-based digital twin representation. Page 7 of 713SFT-108-A-PCT

[0089] In some aspects, the techniques described herein relate to a method, wherein the policy check includes validating the capability declaration against an organizational policy and an external regulation.

[0090] In some aspects, the techniques described herein relate to a method for orchestrating artificial intelligence model interaction in a vehicle system, the method including: executing a configured artificial intelligence system including an artificial intelligence orchestrator system; establishing, by the artificial intelligence orchestrator system, a connection between a first artificial intelligence model and a second artificial intelligence model deployed in a vehicle; initiating, by the artificial intelligence orchestrator system, a session between the first artificial intelligence model and the second artificial intelligence model; exchanging, by the artificial intelligence orchestrator system, an authentication token and an identity token between the first artificial intelligence model and the second artificial intelligence model; performing, by the artificial intelligence orchestrator system, capability recognition wherein the first artificial intelligence model declares a capability and a limitation; and performing, by the artificial intelligence orchestrator system, input preparation and negotiation to format prepared input based on the declared capability.

[0091] In some aspects, the techniques described herein relate to a method, wherein the connection establishment includes a secure handshake between the first artificial intelligence model and the second artificial intelligence model.

[0092] In some aspects, the techniques described herein relate to a method, wherein a secure handshake includes application programming interface key validation.

[0093] In some aspects, the techniques described herein relate to a method, wherein a secure handshake includes an OAuth-like flow.

[0094] In some aspects, the techniques described herein relate to a method, wherein a secure handshake includes cryptographic signature verification.

[0095] In some aspects, the techniques described herein relate to a method, wherein the capability includes a supported input type and a supported output type.

[0096] In some aspects, the techniques described herein relate to a method, wherein the capability includes a supported language specification.

[0097] In some aspects, the techniques described herein relate to a method, wherein the limitation includes a performance constraint.

[0098] In some aspects, the techniques described herein relate to a method, wherein the limitation includes a latency constraint.

[0099] In some aspects, the techniques described herein relate to a method, wherein the limitation includes a policy restriction.

[0100] In some aspects, the techniques described herein relate to a method, wherein the input preparation and negotiation includes ensuring agreement on a data schema.

[0101] In some aspects, the techniques described herein relate to a method, wherein the input preparation and negotiation includes ensuring agreement on a tokenization specification and an encoding specification.

[0102] In some aspects, the techniques described herein relate to a method for generating a digital twin of a physical artificial intelligence system in a vehicle, the method including: executing a configured artificial intelligence system including a digital twin system and a know your physical artificial intelligence system; receiving, by the know your physical artificial intelligence system, discovery information from a physical artificial intelligence device in a vehicle; authenticating, by the know your physical artificial intelligence system, the physical artificial intelligence device using hardware attestation; generating, by the digital twin system, a digital twin representation of the physical artificial Page 8 of 713SFT-108-A-PCT intelligence device based on the discovery information; receiving, by the know your physical artificial intelligence system, a capability declaration from the physical artificial intelligence device; updating, by the digital twin system, the digital twin representation with capability information from the capability declaration; and continuously updating, by the digital twin system, the digital twin representation using data streamed from the physical artificial intelligence device.

[0103] In some aspects, the techniques described herein relate to a method, wherein the discovery information includes a cryptographic identity credential, a device serial number, and a pre-provisioned public key infrastructure certificate.

[0104] In some aspects, the techniques described herein relate to a method, wherein the hardware attestation uses a trusted platform module cryptographic signature.

[0105] In some aspects, the techniques described herein relate to a method, wherein the capability declaration includes a machine-readable manifest describing a supported artificial intelligence task.

[0106] In some aspects, the techniques described herein relate to a method, wherein the capability declaration includes a hardware specification and an operational constraint.

[0107] In some aspects, the techniques described herein relate to a method, wherein the capability declaration includes a certified artificial intelligence model signature.

[0108] In some aspects, the techniques described herein relate to a method, wherein the digital twin representation models intelligent behavior of the physical artificial intelligence device.

[0109] In some aspects, the techniques described herein relate to a method, wherein a digital twin representation models physical component and capability of the physical artificial intelligence device.

[0110] In some aspects, the techniques described herein relate to a method, wherein the data streamed from the physical artificial intelligence device includes validation result and policy configuration.

[0111] In some aspects, the techniques described herein relate to a method, wherein continuously updating the digital twin representation includes reflecting a current state and a compliance status of the physical artificial intelligence device.

[0112] In some aspects, the techniques described herein relate to a method, wherein the digital twin representation includes an operational history of the physical artificial intelligence device.

[0113] In some aspects, the techniques described herein relate to a method, further including using, by the know your physical artificial intelligence system, simulation data involving the digital twin representation as input to an artificial intelligence-based learning model.

[0114] In some aspects, the techniques described herein relate to a computer-implemented method for managing artificial intelligence models in a transportation system, including: executing, by a processor, at least one model of a first set of AI-based learning models to perform model intake actions associated with a second set of AI-based learning models for transportation applications; performing, by the at least one model, a model evaluation action on the second set of AI-based learning models to assess a safety implication for a vehicular environment; executing a model deployment action to integrate the second set of AI-based learning models into transportation infrastructure; implementing a model monitoring action to track performance of a deployed model in a real-time transportation scenario; and performing a model update based on a new traffic pattern.

[0115] In some aspects, the techniques described herein relate to a method, wherein the first set of AI-based learning models includes transformer-based large language models for legal document classification to ensure compliance with a dataset licensing requirement. Page 9 of 713SFT-108-A-PCT

[0116] In some aspects, the techniques described herein relate to a method, wherein the model evaluation includes validation of transportation regulatory compliance and assessment of safety implications for autonomous vehicle operations.

[0117] In some aspects, the techniques described herein relate to a method, wherein the AI-based learning models include at least one of a convolutional neural network, recurrent neural network, or transformer model configured for transportation application.

[0118] In some aspects, the techniques described herein relate to a method, further including processing model documentation, training dataset metadata, and license agreement texts during an intake and registration process.

[0119] In some aspects, the techniques described herein relate to a method, wherein the method outputs at least one of a compliance score, pass / fail status, or reasoning trace for model validation.

[0120] In some aspects, the techniques described herein relate to a method, further including implementing security and privacy validation using multi-agent security validation systems.

[0121] In some aspects, the techniques described herein relate to a method, wherein the model monitoring includes tracking model performance degradation in a transportation context.

[0122] In some aspects, the techniques described herein relate to a method, further including generating a digital twin representation of AI models subject to an intake and registration process.

[0123] In some aspects, the techniques described herein relate to a method, wherein the transportation applications include at least one of autonomous vehicle control, traffic management, or fleet coordination.

[0124] In some aspects, the techniques described herein relate to a method, further including maintaining data provenance tracking throughout AI model lifecycles.

[0125] In some aspects, the techniques described herein relate to a method, wherein the method includes human-in-the- loop evaluation for periodic assessment of model outputs.

[0126] In some aspects, the techniques described herein relate to a computer-implemented method for validating data integrity in transportation systems, including: analyzing, by a processor, a deviation across historical transportation datasets to identify an anomaly indicative of malicious manipulation in at least one of vehicle sensor outputs, traffic monitoring data, or transportation system logs; processing data received from a client input channel; implementing protocol-specific preprocessing; correlating IP geolocation data with a device attestation credential to prevent a spoofing attack by requiring spatial consistency between a network routing path and a hardware provenance marker; and implementing a dynamic data score adjustment based on a threat intelligence update.

[0127] In some aspects, the techniques described herein relate to a method, wherein the historical datasets include vehicle performance records, traffic sensor data, and transportation infrastructure status information.

[0128] In some aspects, the techniques described herein relate to a method, further including processing file upload interfaces receiving CSV exports from transportation databases.

[0129] In some aspects, the techniques described herein relate to a method, wherein the method implements transportation-specific data privacy measures including anonymization of vehicle tracking data.

[0130] In some aspects, the techniques described herein relate to a method, further including ensuring compliance with transportation privacy regulations and vehicle data protection standards.

[0131] In some aspects, the techniques described herein relate to a method, wherein an adaptive filtering mechanism reduces computational overhead compared to static rule-based systems. Page 10 of 713SFT-108-A-PCT

[0132] In some aspects, the techniques described herein relate to a method, further including enabling efficient processing of high-velocity data streams in distributed architectures.

[0133] In some aspects, the techniques described herein relate to a method, wherein the method integrates cryptographic authentication checks directly into content analysis pipelines.

[0134] In some aspects, the techniques described herein relate to a method, further including maintaining data integrity across heterogeneous IoT ecosystems while preserving interoperability.

[0135] In some aspects, the techniques described herein relate to a method, wherein the client input channels include vehicle telemetry systems and traffic monitoring infrastructure.

[0136] In some aspects, the techniques described herein relate to a method, further including implementing real-time correlation of network routing data with hardware authentication credentials.

[0137] In some aspects, the techniques described herein relate to a method, wherein the method addresses technical challenges in legacy industrial control system integration.

[0138] In some aspects, the techniques described herein relate to a computer-implemented method for optimizing powertrain performance using artificial intelligence, including: controlling, by an artificial intelligence system, a powertrain component based on an operational model selected from at least one of a physics model, electrodynamic model, hydrodynamic model, chemical models for energy conversion, or mechanical models for dynamically interacting system components; manipulating a powertrain operating parameter to achieve a desired powertrain state; training the AI system on datasets of outcomes including at least one of fuel efficiency, safety, or rider satisfaction; implementing a hybrid neural network wherein one neural network optimizes a gear shifting operation while another neural network optimizes at least one of a braking, clutch engagement, or energy discharge and recharging; and generating a control instruction consisting of output from at least one component of the hybrid neural network to control a powertrain component.

[0139] In some aspects, the techniques described herein relate to a method, wherein the AI system is trained on datasets of operator actions sensed by sensor sets, cameras, or vehicle information systems.

[0140] In some aspects, the techniques described herein relate to a method, wherein the hybrid neural network optimizes distinct parts of the powertrain using at least two separate neural network components.

[0141] In some aspects, the techniques described herein relate to a method, further including processing social data from multiple social data sources to optimize powertrain operating states.

[0142] In some aspects, the techniques described herein relate to a method, wherein the method processes data sourced from unstructured data sources and wearable devices.

[0143] In some aspects, the techniques described herein relate to a method, further including processing data sourced from in-vehicle sensors and rider helmets.

[0144] In some aspects, the techniques described herein relate to a method, wherein the operational models include predictive models for vehicle maintenance and remaining useful life estimation.

[0145] In some aspects, the techniques described herein relate to a method, further including implementing classification models to predict failure within given time windows.

[0146] In some aspects, the techniques described herein relate to a method, wherein the method includes regression models trained to predict the remaining useful life of vehicle components.

[0147] In some aspects, the techniques described herein relate to a method, further including collecting training data from vehicle specifications, environmental data, sensor data, and operational information. Page 11 of 713SFT-108-A-PCT

[0148] In some aspects, the techniques described herein relate to a method, wherein the AI system stores predictive models in a model datastore within a database system.

[0149] In some aspects, the techniques described herein relate to a method, further including training multiple predictive models to answer different questions regarding powertrain maintenance and optimization.

[0150] In some aspects, the techniques described herein relate to a computer-implemented method for managing transportation system digital twins, including: receiving, by a processor, imported data from one or more data sources corresponding to a transportation system; generating a digital twin of the transportation system representing the system based on the imported data; identifying one or more transportation entities within the transportation system; generating a set of discrete digital twins representing the transportation entities within the transportation system; embedding the set of discrete digital twins within the digital twin of the transportation system; establishing a connection with a sensor system of the transportation system; receiving real-time sensor data from one or more sensors via the connection; and updating at least one of the digital twin of the transportation system and the set of discrete digital twins based on the real-time sensor data.

[0151] In some aspects, the techniques described herein relate to a method, wherein the transportation entities include vehicles, infrastructure components, and traffic management systems.

[0152] In some aspects, the techniques described herein relate to a method, further including implementing executive digital twins for vehicle fleet operations and vehicle digital twins for design and simulation.

[0153] In some aspects, the techniques described herein relate to a method, wherein the method creates enterprise access layers for fleet transactions and comprehensive fleet management.

[0154] In some aspects, the techniques described herein relate to a method, further including implementing contextual simulation and forecasting capabilities through enterprise layers.

[0155] In some aspects, the techniques described herein relate to a method, wherein the method enables modeling of fleet operations, vehicle design scenarios, and enterprise-level transactions.

[0156] In some aspects, the techniques described herein relate to a method, further including leveraging simulations to predict outcomes of operational configurations and optimizing resource allocation.

[0157] In some aspects, the techniques described herein relate to a method, wherein the method forecasts maintenance needs through continuous learning and adaptation.

[0158] In some aspects, the techniques described herein relate to a method, further including refining predictive models based on actual outcomes to improve future simulation accuracy.

[0159] In some aspects, the techniques described herein relate to a method, wherein the sensor system includes LIDAR, cameras, radar, GPS, and vehicle telemetry sensors.

[0160] In some aspects, the techniques described herein relate to a method, further including implementing smart contract systems embedded in digital twins for liability management.

[0161] In some aspects, the techniques described herein relate to a method, wherein the method includes interface systems for designating supervisors for AI systems represented in digital twins.

[0162] In some aspects, the techniques described herein relate to a computer-implemented method for implementing AI agent orchestration in transportation systems, including: deploying, by a processor, an AI agent for transportation applications including at least one of an autonomous vehicle control agent, traffic management agent, or fleet coordination assistant; configuring the AI agent with transportation-specific data; implementing AI orchestration for a transportation environment to enable AI-to-AI and AI-to-machine interactions between at least one of a vehicle Page 12 of 713SFT-108-A-PCT systems, traffic infrastructure, or transportation AI models; facilitating coordination between different AI systems to provide transportation services; reorganizing resources based on changes in demand for processing by AI systems including new types of requests and processing of new data types; and determining a route between AI systems based on evaluation of a request requirement and corresponding features of candidate communication paths.

[0163] In some aspects, the techniques described herein relate to a method, wherein the AI agents are configured with knowledge of traffic regulations, vehicle operation manuals, and transportation safety protocols.

[0164] In some aspects, the techniques described herein relate to a method, further including reorganizing resources based on changes in available and feasible AI systems including large language models and hybrid systems.

[0165] In some aspects, the techniques described herein relate to a method, wherein the method allocates increased computation and storage for models of increased size and computational complexity.

[0166] In some aspects, the techniques described herein relate to a method, further including determining routes based on priority, deadline, data amount, budget, and security considerations.

[0167] In some aspects, the techniques described herein relate to a method, wherein the communication paths include Bluetooth, Wi-Fi, cellular, infrared, and wired Ethernet connections.

[0168] In some aspects, the techniques described herein relate to a method, further including evaluating candidate routes based on throughput, availability, cost, reliability, and security features.

[0169] In some aspects, the techniques described herein relate to a method, wherein the method acquires, purchases, develops, reserves, and provisions communication routes between AI systems.

[0170] In some aspects, the techniques described herein relate to a method, further including reserving communication paths between AI systems intended for high-performance communication.

[0171] In some aspects, the techniques described herein relate to a method, wherein the orchestration handles low- latency AI system processing requests in real-time contexts.

[0172] In some aspects, the techniques described herein relate to a method, further including processing new types of data including LIDAR point-cloud data through orchestrated AI systems.

[0173] In some aspects, the techniques described herein relate to a method, wherein the method manages computational loads and request volumes through dynamic resource allocation.

[0174] In some aspects, the techniques described herein relate to a computer-implemented method for processing multimodal transportation data, including: processing, by multimodal systems, data across multiple formats relevant to transportation including at least one of LIDAR point clouds, camera feeds, radar data, GPS coordinates, vehicle telemetry, traffic sensor data, or route optimization algorithms; enabling situational awareness by analyzing diverse sensor inputs simultaneously; operating foundation and multimodal models designed to process multiple input types, unifying vision, text, and audio under a single architecture; processing at least one of a vehicular sensor data, traffic communications, or navigation instructions in real-time; and creating a transportation-specific data model utilizing at least one of data stores, databases, data warehouses, or data lakes.

[0175] In some aspects, the techniques described herein relate to a method, wherein the multimodal systems integrate vision, text, and audio processing capabilities for comprehensive transportation analysis.

[0176] In some aspects, the techniques described herein relate to a method, further including implementing context- aware sensor fusion to inform analytics and AI processing.

[0177] In some aspects, the techniques described herein relate to a method, wherein the method supports APIs and service-oriented architecture for distributed data processing. Page 13 of 713SFT-108-A-PCT

[0178] In some aspects, the techniques described herein relate to a method, further including processing sensor and energy operations data, market data, environmental data, and alternative data sources.

[0179] In some aspects, the techniques described herein relate to a method, wherein data layer AI capabilities focus on sensor and data fusion for holistic understanding of vehicle operations.

[0180] In some aspects, the techniques described herein relate to a method, further including leveraging machine learning algorithms to analyze transportation networks and optimize vehicle operations.

[0181] In some aspects, the techniques described herein relate to a method, wherein the algorithms include graph neural networks that capture spatial structure of transportation networks.

[0182] In some aspects, the techniques described herein relate to a method, further including implementing reinforcement learning to optimize routing decisions in real-time.

[0183] In some aspects, the techniques described herein relate to a method, wherein the method uses clustering algorithms to segment data based on traffic patterns and geographic features.

[0184] In some aspects, the techniques described herein relate to a method, further including implementing time-series forecasting models to predict future conditions based on historical data.

[0185] In some aspects, the techniques described herein relate to a method, wherein a multi-faceted approach enables analysis of vehicle movement patterns and infrastructure utilization.

[0186] In some aspects, the techniques described herein relate to a computer-implemented method for implementing cognitive charging plans for vehicle fleets, including: processing, by an artificial intelligence system, inputs including predicted traffic conditions for a plurality of vehicles; exchanging information between cloud-based and vehicle-based systems about at least one of vehicle energy consumption, operational information, or recharging infrastructure; responding to transportation system and vehicle information with a control parameter that facilitates executing a cognitive charging plan for charging infrastructure; determining at least one charging plan parameter for at least a portion of the plurality of vehicles is dependent; and executing a program derived based on the charging plan by a processor.

[0187] In some aspects, the techniques described herein relate to a method, wherein the inputs include predicted energy consumption patterns and charging infrastructure availability.

[0188] In some aspects, the techniques described herein relate to a method, further including optimizing charging schedules based on electricity pricing and grid demand patterns.

[0189] In some aspects, the techniques described herein relate to a method, wherein the cognitive charging plan considers vehicle route optimization and destination requirements.

[0190] In some aspects, the techniques described herein relate to a method, further including coordinating charging activities across multiple vehicles to minimize grid impact.

[0191] In some aspects, the techniques described herein relate to a method, wherein the method implements dynamic adjustment of charging parameters based on real-time conditions.

[0192] In some aspects, the techniques described herein relate to a method, further including integrating weather data and traffic predictions into charging plan optimization.

[0193] In some aspects, the techniques described herein relate to a method, wherein the cloud-based systems provide centralized coordination and the vehicle-based systems provide local optimization.

[0194] In some aspects, the techniques described herein relate to a method, further including implementing predictive algorithms for traffic prediction and transportation prediction. Page 14 of 713SFT-108-A-PCT

[0195] In some aspects, the techniques described herein relate to a method, wherein the method includes energy calculation algorithms for optimizing fuel usage and electricity usage.

[0196] In some aspects, the techniques described herein relate to a method, further including optimizing refueling or recharging time, location, and amount based on the charging plan.

[0197] In some aspects, the techniques described herein relate to a method, wherein the method implements vehicle routing algorithms sensitive to vehicle operating parameters and user experience.

[0198] In some aspects, the techniques described herein relate to a computer-implemented method for automated governance of transportation operations, including: implementing, by a processor, policy automation for a vehicle operation within a transportation system; executing regulatory compliance automation to ensure adherence to a transportation regulation; performing reporting automation for oversight of integrated transportation systems; providing automated governance through at least one of a policy enforcement, compliance monitoring, or digital rights management; and adapting a governance model to a detected regulatory requirement change.

[0199] In some aspects, the techniques described herein relate to a method, wherein the policy automation includes enforcement of safety protocols and operational procedures.

[0200] In some aspects, the techniques described herein relate to a method, further including monitoring compliance with vehicle data protection standards and privacy regulations.

[0201] In some aspects, the techniques described herein relate to a method, wherein the regulatory compliance automation adapts to jurisdiction-specific transportation requirements.

[0202] In some aspects, the techniques described herein relate to a method, further including generating automated reports for regulatory authorities and fleet management.

[0203] In some aspects, the techniques described herein relate to a method, wherein the method implements digital rights management for transportation data and AI model usage.

[0204] In some aspects, the techniques described herein relate to a method, further including providing governance across vehicle operations, traffic management, and infrastructure systems.

[0205] In some aspects, the techniques described herein relate to a method, wherein an intelligent governance system learns from operational outcomes to improve policy effectiveness.

[0206] In some aspects, the techniques described herein relate to a method, further including coordinating governance across disconnected technology infrastructure layers.

[0207] In some aspects, the techniques described herein relate to a method, wherein the method reduces manual oversight requirements through automated policy enforcement.

[0208] In some aspects, the techniques described herein relate to a method, further including implementing governance for AI system generation, training, verification, and deployment.

[0209] In some aspects, the techniques described herein relate to a method, wherein an oversight capability extends across enterprise operations and technological resources.

[0210] In some aspects, the techniques described herein relate to a computer-implemented method for vehicle routing optimization using artificial intelligence, including: implementing, by a processor, a vehicle routing algorithm sensitive to at least one of vehicle operating parameters or user experience parameters; processing a traffic prediction algorithm and transportation prediction algorithm to optimize a routing decision; implementing an object detection algorithm for real-time route adjustment based on an environmental condition; calculating an energy parameter for at Page 15 of 713SFT-108-A-PCT least one of optimizing fuel usage, electricity usage, refueling time, location, or amount; and providing a user satisfaction algorithm that calculates a rider preference in a routing decision.

[0211] In some aspects, the techniques described herein relate to a method, wherein the vehicle operating parameters include powertrain efficiency, battery state, and mechanical system status.

[0212] In some aspects, the techniques described herein relate to a method, further including processing user experience parameters including comfort preferences, time constraints, and destination priorities.

[0213] In some aspects, the techniques described herein relate to a method, wherein a genetic algorithm evolves routing solutions based on multiple optimization criteria simultaneously.

[0214] In some aspects, the techniques described herein relate to a method, further including implementing real-time object detection for dynamic route modification based on obstacles and hazards.

[0215] In some aspects, the techniques described herein relate to a method, wherein the energy calculation algorithms consider vehicle-specific consumption patterns and charging infrastructure.

[0216] In some aspects, the techniques described herein relate to a method, further including optimizing routes for multiple vehicles simultaneously to reduce overall system energy consumption.

[0217] In some aspects, the techniques described herein relate to a method, wherein the traffic prediction algorithms use historical data and real-time sensor information.

[0218] In some aspects, the techniques described herein relate to a method, further including implementing machine learning models that adapt routing strategies based on observed outcomes.

[0219] In some aspects, the techniques described herein relate to a method, wherein the method balances multiple objectives including time, energy efficiency, safety, and user satisfaction.

[0220] In some aspects, the techniques described herein relate to a method, further including providing dynamic route updates based on changing traffic conditions and infrastructure status.

[0221] In some aspects, the techniques described herein relate to a method, wherein the user satisfaction algorithms learn individual preferences and adapt routing recommendations accordingly.

[0222] In some aspects, the techniques described herein relate to a computer-implemented method for AI system generation in transportation environments, including: generating, by an AI system generation module, an artificial intelligence system including at least one of a neural networks, machine learning models, or expert systems for a transportation application; provisionally reserving storage capacity for resources of artificial intelligence systems that may be generated in the future; storing resources for generating artificial intelligence systems including at least one of interpretable scripts, compliable source code repositories, executable code modules, and declarative hyperparameter sets; curating a stored artificial intelligence system based on generating a new system to replace a less performant system; configuring generative AI systems to create content for a transportation application and; and generating AI systems that operate independently and together to present transportation-related content.

[0223] In some aspects, the techniques described herein relate to a method, wherein the neural networks include convolutional neural networks for image processing and recurrent neural networks for sequential data.

[0224] In some aspects, the techniques described herein relate to a method, further including generating expert systems with domain-specific knowledge of transportation regulations and safety protocols.

[0225] In some aspects, the techniques described herein relate to a method, wherein the storage reservation includes capacity planning for different types of AI models and their computational requirements. Page 16 of 713SFT-108-A-PCT

[0226] In some aspects, the techniques described herein relate to a method, further including maintaining version control and dependency management for stored AI system resources.

[0227] In some aspects, the techniques described herein relate to a method, wherein a curation process includes performance monitoring and automated replacement of underperforming systems.

[0228] In some aspects, the techniques described herein relate to a method, further including implementing discriminator networks that identify artifacts indicating synthetic content provenance.

[0229] In some aspects, the techniques described herein relate to a method, wherein the generative AI systems create training data, documentation, and operational procedures for transportation systems.

[0230] In some aspects, the techniques described herein relate to a method, further including configuring AI systems to alter content based on review feedback and generate replacement content.

[0231] In some aspects, the techniques described herein relate to a method, wherein the method supports retraining of AI systems to address issues identified during content review.

[0232] In some aspects, the techniques described herein relate to a method, further including generating combinations of AI systems that collaborate on complex transportation planning tasks.

[0233] In some aspects, the techniques described herein relate to a method, wherein the AI system generation adapts to emerging transportation technologies and changing operational requirements.

[0234] In some aspects, the techniques described herein relate to a computer-implemented method for physical AI system integration in transportation, including: performing, by a processor, automated onboarding and deployment of a physical AI systems within a transportation system of systems environment; enabling secure AI-powered physical devices including at least one of a mobile robot, autonomous surveillance unit, intelligent delivery vehicle, or aerial drone; implementing discovery and authentication actions for physical AI systems upon initial connection; broadcasting discovery packets including at least one of cryptographic identity credentials, device serial numbers, and pre-provisioned public key infrastructure certificates; performing hardware attestation using at least one of trusted platform modules or secure enclave cryptographic signatures to verify device integrity and authenticity; and establishing an encrypted operational session.

[0235] In some aspects, the techniques described herein relate to a method, wherein the physical AI systems include autonomous vehicles, traffic monitoring devices, and infrastructure control systems.

[0236] In some aspects, the techniques described herein relate to a method, further including implementing mutual TLS or zero-trust network onboarding standards for secure communication.

[0237] In some aspects, the techniques described herein relate to a method, wherein the discovery packets are transmitted via secure networking protocols with cryptographic verification.

[0238] In some aspects, the techniques described herein relate to a method, further including validating device certificates against trusted certificate authorities and revocation lists.

[0239] In some aspects, the techniques described herein relate to a method, wherein the hardware attestation includes verification of firmware integrity and hardware security modules.

[0240] In some aspects, the techniques described herein relate to a method, further including implementing device identity management and lifecycle tracking for physical AI systems.

[0241] In some aspects, the techniques described herein relate to a method, wherein the encrypted operational sessions use ephemeral keys and forward secrecy protocols. Page 17 of 713SFT-108-A-PCT

[0242] In some aspects, the techniques described herein relate to a method, further including integrating with know your model systems to manage AI model lifecycles for physical devices.

[0243] In some aspects, the techniques described herein relate to a method, wherein the method enables secure interaction with orchestration components and backend services.

[0244] In some aspects, the techniques described herein relate to a method, further including implementing policy enforcement for physical AI system operations and data access.

[0245] In some aspects, the techniques described herein relate to a method, wherein a context-aware integration considers environmental conditions and operational requirements.

[0246] In some aspects, the techniques described herein relate to a computer-implemented method for AI model interpretability in transportation systems, including: organizing, by a configured artificial intelligence system, an AI agent to utilize AI models that facilitate interpretability over those that do not; using the AI agent to adopt interpretable AI models including at least one of shallow artificial neural networks, decision trees, or convolutional neural networks; preserving metadata relating to AI agent actions in a context of given inputs and corresponding outputs; and generating a documentation of the AI agent's actions and outputs as at least one of narrative descriptions, flowcharts, or interpretable AI models.

[0247] In some aspects, the techniques described herein relate to a method, wherein the interpretable AI models are specifically configured for vehicle control and traffic management decisions.

[0248] In some aspects, the techniques described herein relate to a method, further including generating decision trees that explain routing choices and safety-critical decisions.

[0249] In some aspects, the techniques described herein relate to a method, wherein a stepwise reasoning preservation includes timestamps and decision context for audit trails.

[0250] In some aspects, the techniques described herein relate to a method, further including maintaining reasoning traces for regulatory compliance and accident investigation.

[0251] In some aspects, the techniques described herein relate to a method, wherein an explanation include natural language descriptions of AI decision-making processes.

[0252] In some aspects, the techniques described herein relate to a method, further including generating visual flowcharts that illustrate AI reasoning paths for human operators.

[0253] In some aspects, the techniques described herein relate to a method, wherein the interpretability requirements are tailored to transportation safety standards and regulations.

[0254] In some aspects, the techniques described herein relate to a method, further including implementing explainable AI techniques for autonomous vehicle decision validation.

[0255] In some aspects, the techniques described herein relate to a method, wherein the method supports real-time explanation generation for critical transportation decisions.

[0256] In some aspects, the techniques described herein relate to a method, further including providing interpretability interfaces for transportation system operators and regulators.

[0257] In some aspects, the techniques described herein relate to a method, wherein the interpretable models maintain performance while providing transparency for safety-critical applications.

[0258] In some aspects, the techniques described herein relate to a computer-implemented method for cross-service resource optimization in transportation systems, including: managing, by a cross-service resource optimization system including AI agents, configurable integration capabilities across internal subsystems and services of a transportation Page 18 of 713SFT-108-A-PCT platform; learning interfaces of subsystems from different platforms and generating data and network connections among them; generating interfaces by which users or AI agents may manage data and network connections between integrated subsystems; integrating data and network communication, data storage, management interfaces, and energy resources across platforms; translating isolated platforms into converged platforms through parallel connectivity across internal subsystems; and evolving from pairwise converged platforms to converged platforms that encompass subsystem elements across integrated platforms.

[0259] In some aspects, the techniques described herein relate to a method, wherein the transportation platforms include vehicle control systems, traffic management systems, and fleet coordination platforms. The method, further including implementing AI agents trained to understand platform-specific interfaces and communication protocols.

[0260] In some aspects, the techniques described herein relate to a method, wherein the data and network connections enable real-time information sharing between previously isolated systems.

[0261] In some aspects, the techniques described herein relate to a method, further including providing unified management interfaces for controlling integrated transportation subsystems.

[0262] In some aspects, the techniques described herein relate to a method, wherein the energy resource integration optimizes power distribution across connected transportation platforms.

[0263] In some aspects, the techniques described herein relate to a method, further including implementing scalable integration that supports addition of new transportation platforms.

[0264] In some aspects, the techniques described herein relate to a method, wherein a massively parallel connectivity enables simultaneous operation of multiple transportation services.

[0265] In some aspects, the techniques described herein relate to a method, further including managing configuration, deployment, provisioning, and optimization of integrated subsystems.

[0266] In some aspects, the techniques described herein relate to a method, wherein the converged platform maintains operational efficiency while expanding integration capabilities.

[0267] In some aspects, the techniques described herein relate to a method, further including implementing security measures that protect integrated systems while enabling seamless communication.

[0268] In some aspects, the techniques described herein relate to a method, wherein an evolution to full convergence maintains backward compatibility with existing transportation systems.

[0269] In some aspects, the techniques described herein relate to a computer-implemented method for transportation system learning and adaptation, including: providing an AI convergence system of systems to track operational data and outcomes of a transportation system; implementing a contextual simulation and forecasting capabilities through enterprise layers to enable modeling at least one of fleet operations, vehicle design scenarios, and enterprise-level transactions; and predicting outcomes based on the simulation and forecasting relating to at least one of operational configurations, optimize resource allocation, and forecast maintenance needs.

[0270] In some aspects, the techniques described herein relate to a method, wherein the operational data includes vehicle performance metrics, traffic patterns, and user behavior analytics.

[0271] In some aspects, the techniques described herein relate to a method, further including implementing continuous learning algorithms that adapt to changing transportation environments.

[0272] In some aspects, the techniques described herein relate to a method, wherein a new technology adoption includes integration of emerging sensors, communication protocols, and AI models. Page 19 of 713SFT-108-A-PCT

[0273] In some aspects, the techniques described herein relate to a method, further including maintaining system scalability through cloud-based and distributed computing architectures.

[0274] In some aspects, the techniques described herein relate to a method, wherein a modular design enables independent updates and enhancements to system components.

[0275] In some aspects, the techniques described herein relate to a method, further including implementing version control and compatibility management for system modules.

[0276] In some aspects, the techniques described herein relate to a method, wherein an adaptive learning mechanism includes reinforcement learning and transfer learning techniques.

[0277] In some aspects, the techniques described herein relate to a method, further including refining predictive models based on actual outcomes to improve future simulation accuracy.

[0278] In some aspects, the techniques described herein relate to a method, wherein the contextual simulation considers environmental conditions, regulatory changes, and market dynamics.

[0279] In some aspects, the techniques described herein relate to a method, further including implementing feedback loops that enable continuous system improvement based on performance metrics.

[0280] In some aspects, the techniques described herein relate to a method, wherein the forecasting capabilities support proactive maintenance scheduling and resource planning.

[0281] In some aspects, the techniques described herein relate to a computer-implemented method for transportation data analysis and modeling, including: implementing, by a processor, a data layer providing context-aware sensor fusion data; processing at least one of sensor data, energy operations data, market data, environmental data, and alternative data sources through a service-oriented architecture; analyzing a transportation network using a machine learning algorithm including a graph neural network related to a spatial structure of a transportation network; implementing reinforcement learning to optimize a routing decision in real-time; implementing a clustering algorithm to segment data based on at least one of a traffic pattern or geographic feature; utilizing a time-series forecasting model to predict a future condition based on historical data; and enabling analysis of vehicle movement patterns.

[0282] In some aspects, the techniques described herein relate to a method, wherein the context-aware sensor fusion combines data from LIDAR, cameras, radar, GPS, and vehicle telemetry systems.

[0283] In some aspects, the techniques described herein relate to a method, further including implementing distributed data processing capabilities for handling high-volume transportation data streams.

[0284] In some aspects, the techniques described herein relate to a method, wherein the graph neural networks model complex relationships between transportation network nodes and edges.

[0285] In some aspects, the techniques described herein relate to a method, further including using reinforcement learning agents that learn optimal routing policies through interaction with traffic environments.

[0286] In some aspects, the techniques described herein relate to a method, wherein the clustering algorithms identify patterns in vehicle behavior, traffic flow, and infrastructure usage.

[0287] In some aspects, the techniques described herein relate to a method, further including implementing time-series models that account for seasonal variations and trend analysis in transportation data.

[0288] In some aspects, the techniques described herein relate to a method, wherein a multi-faceted analysis combines statistical methods, machine learning, and domain expertise.

[0289] In some aspects, the techniques described herein relate to a method, further including providing real-time analytics capabilities for immediate decision-making support. Page 20 of 713SFT-108-A-PCT

[0290] In some aspects, the techniques described herein relate to a method, wherein the method supports both batch processing and stream processing of transportation data.

[0291] In some aspects, the techniques described herein relate to a method, further including implementing data quality assessment and anomaly detection for transportation datasets.

[0292] In some aspects, the techniques described herein relate to a method, wherein the analysis results inform policy decisions, infrastructure planning, and operational optimization.

[0293] In some aspects, the techniques described herein relate to a computer-implemented method for safety assurance in autonomous transportation systems, including: integrating, by a processor, know your data (KYD) and know your model (KYM) capabilities to provide safety assurance for autonomous vehicles and transportation infrastructure; validating incoming sensor and communication data through the KYD system while confirming AI models processing the data meet safety and performance standards through the KYM system; implementing AI-enabled data provenance engines and associated rules that provide tracking, validation, and governance of data throughout AI and machine learning system lifecycles for transportation applications; implementing mechanisms for regular human-in-the-loop evaluation and external audits where human reviewers and domain experts periodically assess model outputs for subtle degradations; maintaining data provenance tracking where an intelligence system logs detailed metadata on data origin, source credibility, and whether data instances are model-generated or human-produced; and preventing degradation of transportation AI models through comprehensive monitoring and validation processes.

[0294] In some aspects, the techniques described herein relate to a method, wherein the safety assurance includes validation of decision-making accuracy and assessment of safety implications for vehicular environments.

[0295] In some aspects, the techniques described herein relate to a method, further including implementing real-time monitoring of AI model performance in safety-critical transportation scenarios.

[0296] In some aspects, the techniques described herein relate to a method, wherein the KYD system validates sensor data integrity and detects anomalies in vehicle telemetry and traffic monitoring data.

[0297] In some aspects, the techniques described herein relate to a method, further including ensuring KYM system compliance with transportation regulatory requirements and safety standards.

[0298] In some aspects, the techniques described herein relate to a method, wherein the data provenance engines track data lineage from sensors through processing pipelines to decision outputs.

[0299] In some aspects, the techniques described herein relate to a method, further including implementing governance rules that enforce data quality standards and model performance thresholds.

[0300] In some aspects, the techniques described herein relate to a method, wherein the human-in-the-loop evaluation includes assessment of factual accuracy, semantic richness, and safety performance.

[0301] In some aspects, the techniques described herein relate to a method, further including conducting external audits by transportation safety experts and regulatory authorities.

[0302] In some aspects, the techniques described herein relate to a method, wherein the data provenance tracking includes timestamps, processing history, and quality metrics for all data instances.

[0303] In some aspects, the techniques described herein relate to a method, further including implementing automated alerts and corrective actions when model degradation is detected.

[0304] In some aspects, the techniques described herein relate to a method, wherein the comprehensive monitoring includes performance metrics, safety indicators, and compliance status tracking. Page 21 of 713SFT-108-A-PCT

[0305] In some aspects, the techniques described herein relate to a transportation artificial intelligence module system including: an AI agent including at least one of an autonomous vehicle control agent, traffic management agent, or fleet coordination assistant configured with transportation-specific knowledge; a know your model (KYM) system for managing AI models in vehicular environments that performs model intake and registration for transportation AI models; and a governance module enforcing at least one governance rule related to a transportation regulatory standard.

[0306] In some aspects, the techniques described herein relate to a system, wherein the autonomous vehicle control agents process real-time sensor data for navigation decisions.

[0307] In some aspects, the techniques described herein relate to a system, wherein the traffic management agents optimize traffic flow and incident response.

[0308] In some aspects, the techniques described herein relate to a system, wherein the fleet coordination assistants manage multi-vehicle operations and scheduling.

[0309] In some aspects, the techniques described herein relate to a system, wherein the KYM system ensures transportation AI models meet NHTSA safety standards.

[0310] In some aspects, the techniques described herein relate to a system, wherein a model intake process includes validation of transportation-specific performance metrics.

[0311] In some aspects, the techniques described herein relate to a system, wherein a safety validation framework includes testing under various driving conditions.

[0312] In some aspects, the techniques described herein relate to a system, wherein the regulatory compliance monitoring ensures adherence to DOT guidelines.

[0313] In some aspects, the techniques described herein relate to a system, wherein a traffic safety oversight includes real-time monitoring of AI decision-making.

[0314] In some aspects, the techniques described herein relate to a system, wherein the AI agents adapt to different transportation environments and conditions.

[0315] In some aspects, the techniques described herein relate to a system, wherein the KYM system validates AI models for deployment in safety-critical applications.

[0316] In some aspects, the techniques described herein relate to a system, wherein the governance modules implement automated compliance checking.

[0317] In some aspects, the techniques described herein relate to a system, wherein the system ensures AI models operate within defined safety parameters.

[0318] In some aspects, the techniques described herein relate to a system, wherein the transportation-specific knowledge includes traffic laws and vehicle operation protocols.

[0319] In some aspects, the techniques described herein relate to a system, wherein the AI agents coordinate with human operators for optimal transportation outcomes.

[0320] In some aspects, the techniques described herein relate to a system, wherein the KYM system provides continuous monitoring of AI model performance in vehicles.

[0321] In some aspects, the techniques described herein relate to a system, wherein a governance framework adapts to changing transportation regulations.

[0322] In some aspects, the techniques described herein relate to a system, wherein the system supports both individual vehicle and fleet-level AI operations. Page 22 of 713SFT-108-A-PCT

[0323] In some aspects, the techniques described herein relate to a system, wherein the AI modules integrate with existing transportation management systems.

[0324] In some aspects, the techniques described herein relate to a transportation governance layer for AI convergence systems including: governance module storing transportation regulations including at least one of DOT guidelines, NHTSA safety standards, or international vehicle safety protocols; AI governor monitoring actions of AI system during both training and inference phases; and a policy engine that serves as a central control mechanism that defines, stores, and enforces governance policies across the AI system specifically tailored for transportation safety requirements.

[0325] In some aspects, the techniques described herein relate to a system, wherein the governance modules implement data privacy governance for vehicle and passenger data protection.

[0326] In some aspects, the techniques described herein relate to a system, wherein the system implements differential privacy mechanisms that add calibrated noise to AI / ML model outputs.

[0327] In some aspects, the techniques described herein relate to a system, wherein the policy engine enforces governance policies during AI system training phases.

[0328] In some aspects, the techniques described herein relate to a system, wherein the AI governors monitor AI system behavior during inference phases.

[0329] In some aspects, the techniques described herein relate to a system, wherein a specialized governance module ensures compliance with DOT guidelines.

[0330] In some aspects, the techniques described herein relate to a system, wherein the system enforces NHTSA safety standards across transportation AI systems.

[0331] In some aspects, the techniques described herein relate to a system, wherein the governance policies are specifically tailored for autonomous vehicle operations.

[0332] In some aspects, the techniques described herein relate to a system, wherein the control mechanisms regulate AI behavior in real-time transportation scenarios.

[0333] In some aspects, the techniques described herein relate to a system, wherein the policy engine stores governance policies in a centralized repository.

[0334] In some aspects, the techniques described herein relate to a system, wherein the system defines governance policies for different transportation modalities.

[0335] In some aspects, the techniques described herein relate to a system, wherein the governance modules prevent individual record identification while preserving analytical utility.

[0336] In some aspects, the techniques described herein relate to a system, wherein the system implements regulation governance modules for vehicle data protection.

[0337] In some aspects, the techniques described herein relate to a system, wherein the AI governors constrain AI system behavior based on safety requirements.

[0338] In some aspects, the techniques described herein relate to a system, wherein a governance framework supports traffic pattern analysis and transportation research.

[0339] In some aspects, the techniques described herein relate to a system, wherein the policy engine adapts to changing regulatory requirements automatically.

[0340] In some aspects, the techniques described herein relate to a system, wherein the specialized governance modules integrate with existing transportation management systems. Page 23 of 713SFT-108-A-PCT

[0341] In some aspects, the techniques described herein relate to a system, wherein the system maintains compliance across multiple jurisdictional requirements.

[0342] In some aspects, the techniques described herein relate to a system, wherein the governance policies support both cloud-based and edge-deployed AI systems.

[0343] In some aspects, the techniques described herein relate to a transportation offering system including: location- based offering system providing contextual services based on a vehicle position and environmental condition; advertising integration for targeted content delivery within transportation environments; and a customer-facing vehicle digital twin providing personalized transportation experiences and real-time vehicle status information to users based on the vehicle position and environmental condition.

[0344] In some aspects, the techniques described herein relate to a system, wherein the location-based services adapt to changing environmental conditions.

[0345] In some aspects, the techniques described herein relate to a system, wherein the advertising integration provides contextually relevant content to vehicle occupants.

[0346] In some aspects, the techniques described herein relate to a system, wherein the customer-facing digital twins provide real-time vehicle performance data.

[0347] In some aspects, the techniques described herein relate to a system, wherein the location-based offerings include route-specific services and recommendations.

[0348] In some aspects, the techniques described herein relate to a system, wherein a marketplace integration enables in-vehicle commerce and service booking.

[0349] In some aspects, the techniques described herein relate to a system, wherein the digital twins provide personalized transportation experiences based on user preferences.

[0350] In some aspects, the techniques described herein relate to a system, wherein the system delivers targeted content based on vehicle location and user profile.

[0351] In some aspects, the techniques described herein relate to a system, wherein the customer-facing services include real-time traffic and navigation updates.

[0352] In some aspects, the techniques described herein relate to a system, wherein the location-based system provides contextual information about nearby services.

[0353] In some aspects, the techniques described herein relate to a system, wherein an advertising platform integrates with vehicle entertainment systems.

[0354] In some aspects, the techniques described herein relate to a system, wherein the digital twins enable remote vehicle monitoring and control capabilities.

[0355] In some aspects, the techniques described herein relate to a system, wherein an offering layer customizes services based on transportation patterns and preferences.

[0356] In some aspects, the techniques described herein relate to a system, wherein the system provides location-aware safety alerts and warnings.

[0357] In some aspects, the techniques described herein relate to a system, wherein a marketplace integration supports mobility-as-a-service transactions.

[0358] In some aspects, the techniques described herein relate to a system, wherein the customer-facing interfaces provide intuitive vehicle interaction capabilities. Page 24 of 713SFT-108-A-PCT

[0359] In some aspects, the techniques described herein relate to a system, wherein the location-based services integrate with smart city infrastructure.

[0360] In some aspects, the techniques described herein relate to a system, wherein the system enables personalized content delivery based on travel context.

[0361] In some aspects, the techniques described herein relate to a system, wherein an offering layer supports both individual and shared transportation services.

[0362] In some aspects, the techniques described herein relate to a transportation transactions layer system including: processor for data transfer between vehicles, infrastructure, and transportation systems, wherein the data transfer supports digital transactions including at least one of vehicle-to-everything (V2X) communication protocols, traffic data sharing, and interoperability with external transportation networks; API gateway managing data ingress points for transportation data, and serving as enforcement node for at least one of safety compliance or vehicle data validation; and governance module for cryptographic validation of data sources to confirm vehicle sensor data integrity and authenticity.

[0363] In some aspects, the techniques described herein relate to a system, wherein the V2X communication protocols enable secure inter-vehicle data exchange.

[0364] In some aspects, the techniques described herein relate to a system, wherein the API gateways implement cryptographic validation for transportation data sources.

[0365] In some aspects, the techniques described herein relate to a system, wherein the system ensures traffic data sharing integrity across transportation networks.

[0366] In some aspects, the techniques described herein relate to a system, wherein a cryptographic attestation prevents unauthorized access to vehicle sensor data.

[0367] In some aspects, the techniques described herein relate to a system, wherein the transaction system supports interoperability with external transportation platforms.

[0368] In some aspects, the techniques described herein relate to a system, wherein the data validation includes real- time verification of vehicle communications.

[0369] In some aspects, the techniques described herein relate to a system, wherein the API gateways enforce safety compliance for all transportation data exchanges.

[0370] In some aspects, the techniques described herein relate to a system, wherein the system manages secure communication channels for vehicle data transmission.

[0371] In some aspects, the techniques described herein relate to a system, wherein the V2X protocols support emergency vehicle communication prioritization.

[0372] In some aspects, the techniques described herein relate to a system, wherein the cryptographic requirements ensure data authenticity throughout transmission.

[0373] In some aspects, the techniques described herein relate to a system, wherein the transaction layer enables secure integration with traffic management systems.

[0374] In some aspects, the techniques described herein relate to a system, wherein the system provides tamper-evident data exchange for transportation safety.

[0375] In some aspects, the techniques described herein relate to a system, wherein the API gateways support high- throughput transportation data processing. Page 25 of 713SFT-108-A-PCT

[0376] In some aspects, the techniques described herein relate to a system, wherein a secure exchange includes vehicle- to-infrastructure communication protocols.

[0377] In some aspects, the techniques described herein relate to a system, wherein the system maintains data integrity across distributed transportation networks.

[0378] In some aspects, the techniques described herein relate to a system, wherein the cryptographic attestation includes hardware-based security validation.

[0379] In some aspects, the techniques described herein relate to a system, wherein the transaction layer supports real- time data validation for autonomous vehicles.

[0380] In some aspects, the techniques described herein relate to a system, wherein the system enables secure data sharing between different transportation authorities.

[0381] In some aspects, the techniques described herein relate to a computer-implemented method substantially as hereinbefore described with reference to any of the examples and / or to the attached drawings.

[0382] In some aspects, the techniques described herein relate to a computing system including one or more processors and one or more memories configured to perform operations substantially as hereinbefore described with reference to any of the examples and / or to the attached drawings.

[0383] In some aspects, the techniques described herein relate to a computer program product residing on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations substantially as hereinbefore described with reference to any of the examples and / or to the attached drawings.

[0384] In some aspects, the techniques described herein relate to a device configured substantially as hereinbefore described with reference to any of the examples and / or to the attached drawings.

[0385] It is to be understood that any combination of features from the methods disclosed herein and / or from the systems disclosed herein may be used together, and / or that any features from any or all of these aspects may be combined with any of the features of the embodiments and / or examples disclosed herein to achieve the benefits as described in this disclosure. BRIEF DESCRIPTION OF THE FIGURES

[0386] In the accompanying figures, like reference numerals refer to identical or functionally similar elements throughout the separate views and together with the detailed description below are incorporated in and form part of the specification, serve to further illustrate various embodiments and to explain various principles and advantages all in accordance with the systems and methods disclosed herein.

[0387] FIG. 1 is a diagrammatic view that illustrates an architecture for a transportation system showing certain illustrative components and arrangements relating to various embodiments of the present disclosure.

[0388] FIG.2 is a diagrammatic view that illustrates use of a hybrid neural network to optimize a powertrain component of a vehicle relating to various embodiments of the present disclosure.

[0389] FIG.3 is a diagrammatic view that illustrates a set of states that may be provided as inputs to and / or be governed by an expert system / Artificial Intelligence (AI) system relating to various embodiments of the present disclosure.

[0390] FIG. 4 is a diagrammatic view that illustrates a range of parameters that may be taken as inputs by an expert system or AI system, or component thereof, as described throughout this disclosure, or that may be provided as outputs from such a system and / or one or more sensors, cameras, or external systems relating to various embodiments of the present disclosure. Page 26 of 713SFT-108-A-PCT

[0391] FIG.5 is a diagrammatic view that illustrates a set of vehicle user interfaces relating to various embodiments of the present disclosure.

[0392] FIG. 6 is a diagrammatic view that illustrates a set of interfaces among transportation system components relating to various embodiments of the present disclosure.

[0393] FIG.7 is a diagrammatic view that illustrates a data processing system, which may process data from various sources relating to various embodiments of the present disclosure.

[0394] FIG.8 is a diagrammatic view that illustrates a set of algorithms that may be executed in connection with one or more of the many embodiments of transportation systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0395] FIG. 9 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0396] FIG.10 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0397] FIG.11 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0398] FIG.12 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0399] FIG.13 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0400] FIG.14 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0401] FIG.15 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0402] FIG.16 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0403] FIG.17 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0404] FIG.18 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0405] FIG.19 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0406] FIG.20 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0407] FIG.21 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0408] FIG.22 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0409] FIG.23 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure. Page 27 of 713SFT-108-A-PCT

[0410] FIG.24 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0411] FIG.25 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0412] FIG.26 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0413] FIG.26A is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0414] FIG.27 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0415] FIG.28 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0416] FIG.29 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0417] FIG.30 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0418] FIG.31 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0419] FIG.32 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0420] FIG.33 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0421] FIG.34 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0422] FIG.35 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0423] FIG.36 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0424] FIG.37 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0425] FIG.38 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0426] FIG.39 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0427] FIG.40 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0428] FIG.41 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0429] FIG.42 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure. Page 28 of 713SFT-108-A-PCT

[0430] FIG.43 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0431] FIG.44 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0432] FIG.45 is a diagrammatic view that illustrates systems and methods described throughout this disclosure relating to various embodiments of the present disclosure.

[0433] FIG.46 is a diagrammatic view that illustrates systems and methods described throughout this disclosure relating to various embodiments of the present disclosure.

[0434] FIG.47 is a diagrammatic view that illustrates systems and methods described throughout this disclosure relating to various embodiments of the present disclosure.

[0435] FIG.48 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0436] FIG.49 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0437] FIG.50 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0438] FIG.51 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0439] FIG.52 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0440] FIG.53 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0441] FIG.54 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0442] FIG.55 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0443] FIG.56 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0444] FIG.57 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0445] FIG.58 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0446] FIG. 59 is a diagrammatic view that illustrates an architecture for a transportation system including a digital twin system of a vehicle showing certain illustrative components and arrangements relating to various embodiments of the present disclosure.

[0447] FIG. 60 shows a schematic illustration of the digital twin system integrated with an identity and access management system in accordance with certain embodiments of the present disclosure.

[0448] FIG.61 illustrates a schematic view of an interface of the digital twin system presented on the user device of a driver of the vehicle relating to various embodiments of the present disclosure. Page 29 of 713SFT-108-A-PCT

[0449] FIG. 62 is a schematic diagram showing the interaction between the driver and the digital twin using one or more views and modes of the interface in accordance with an example embodiment of the present disclosure.

[0450] FIG.63 illustrates a schematic view of an interface of the digital twin system presented on the user device of a manufacturer of the vehicle in accordance with various embodiments of the present disclosure.

[0451] FIG. 64 depicts a scenario in which the manufacturer uses the quality view of a digital twin interface to run simulations and generate what-if scenarios for quality testing a vehicle in accordance with an example embodiment of the present disclosure.

[0452] FIG.65 illustrates a schematic view of an interface of the digital twin system presented on the user device of a dealer of the vehicle.

[0453] FIG.66 is a diagram illustrating the interaction between the dealer and the digital twin using one or more views with the goal of personalizing the experience of a customer purchasing a vehicle in accordance with an example embodiment.

[0454] FIG. 67 is a diagram illustrating the service & maintenance view presented to a user of a vehicle including a driver, a manufacturer and a dealer of the vehicle in accordance with various embodiments of the present disclosure.

[0455] FIG.68 is a method used by the digital twin for detecting faults and predicting any future failures of the vehicle in accordance with an example embodiment.

[0456] FIG. 69 is a diagrammatic view that illustrates the architecture of a vehicle with a digital twin system for performing predictive maintenance on a vehicle in accordance with an example embodiment of the present disclosure.

[0457] FIG.70 is a flow chart depicting a method for generating a digital twin of a vehicle in accordance with various embodiments of the disclosure.

[0458] FIG.71 is a diagrammatic view that illustrates an alternate architecture for a transportation system comprising a vehicle and a digital twin system in accordance with various embodiments of the present disclosure.

[0459] FIG.72 depicts a digital twin representing a combination of a set of states of both a vehicle and a driver of the vehicle in accordance with certain embodiments of the present disclosure.

[0460] FIG. 73 illustrates a schematic diagram depicting a scenario in which the integrated vehicle and driver digital twin may configure the vehicle experience in accordance with an example embodiment.

[0461] FIG.74 is a schematic illustrating an example of a portion of an information technology system for transportation artificial intelligence leveraging digital twins according to some embodiments of the present disclosure.

[0462] FIG.75 is a schematic illustrating examples of architecture of a digital twin system according to embodiments of the present disclosure.

[0463] FIG. 76 is a schematic illustrating exemplary components of a digital twin management system according to embodiments of the present disclosure.

[0464] FIG.77 is a schematic illustrating examples of a digital twin I / O system that interfaces with an environment, the digital twin system, and / or components thereof to provide bi-directional transfer of data between coupled components according to embodiments of the present disclosure.

[0465] FIG. 78 is a schematic illustrating an example set of identified states related to transportation systems that the digital twin system may identify and / or store for access by intelligent systems (e.g., a cognitive intelligence system) or users of the digital twin system according to embodiments of the present disclosure.

[0466] FIG.79 is a schematic illustrating example embodiments of methods for updating a set of properties of a digital twin of the present disclosure on behalf of a client application and / or one or more embedded digital twins. Page 30 of 713SFT-108-A-PCT

[0467] FIG. 80 illustrates example embodiments of a display interface of the present disclosure that renders a digital twin of a dryer centrifuge with information relating to the dryer centrifuge.

[0468] FIG.81 is a schematic illustrating an example embodiment of a method for updating a set of vibration fault level states of machine components such as bearings in the digital twin of a machine, on behalf of a client application.

[0469] FIG.82 is a schematic illustrating an example embodiment of a method for updating a set of vibration severity unit values of machine components such as bearings in the digital twin of a machine on behalf of a client application.

[0470] FIG. 83 is a schematic illustrating an example embodiment of a method for updating a set of probability of failure values in the digital twins of machine components on behalf of a client application.

[0471] FIG. 84 is a schematic illustrating an example embodiment of a method for updating a set of probability of downtime values of machines in the digital twin of a transportation system on behalf of a client application.

[0472] FIG.85 is a schematic illustrating an example embodiment of a method for updating one or more probability of shutdown values of transportation entities in one or more transportation system digital twins.

[0473] FIG.86 is a schematic illustrating an example embodiment of a method for updating a set of cost of downtime values of machines in the digital twin of a transportation system.

[0474] FIG.87 is a schematic illustrating an example embodiment of a method for updating one or more KPI values in a digital twin of a transportation system, on behalf of a client application.

[0475] FIG.88 is a schematic illustrating an example embodiment of a method of the present disclosure.

[0476] FIG. 89 is a schematic illustrating examples of different types of enterprise digital twins, including executive digital twins, in relation to the data layer, processing layer, and application layer of an enterprise digital twin framework according to some embodiments of the present disclosure.

[0477] FIG.90 is a schematic illustrating an example of a method for configuring role-based digital twins according to some embodiments of the present disclosure.

[0478] FIG.91 is a schematic illustrating an example of a method for configuring a digital twin of a workforce according to some embodiments of the present disclosure.

[0479] FIG.92 is a schematic view of an exemplary embodiment of the quantum computing service according to some embodiments of the present disclosure.

[0480] FIG.93 illustrates quantum computing service request handling according to some embodiments of the present disclosure.

[0481] FIG.94 is a diagrammatic view that illustrates embodiments of the biology-based system in accordance with the present disclosure.

[0482] FIG.95 is a diagrammatic view of the thalamus service and how it coordinates within the modules in accordance with the present disclosure.

[0483] FIG.96 is a diagrammatic view of the dual process artificial neural network system.

[0484] Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of the many embodiments of the systems and methods disclosed herein.

[0485] FIG.97 is a diagrammatic view of artificial intelligence capabilities, convergence technology stack capabilities and software-defined vehicle modules of a transportation system.

[0486] FIG.98 is a diagrammatic view of software defined vehicle modules of a transportation system. Page 31 of 713SFT-108-A-PCT

[0487] FIG. 99 depicts a block diagram of exemplary features, capabilities, and interfaces of a generative artificial intelligence platform of a transportation system.

[0488] FIG.100 is a diagrammatic view of data and visualization methods and systems of a transportation system.

[0489] FIG.101 is a diagrammatic view of data and visualization methods and systems of a transportation system.

[0490] FIG.102 is a schematic view of an example AI convergence system of systems.

[0491] FIG.103 is a schematic view of an example offering layer.

[0492] FIG.104 is a schematic view of an example transactions layer.

[0493] FIG.105 is a schematic view of an example operations layer.

[0494] FIG.106 is a schematic view of an example network layer.

[0495] FIG.107 is a schematic view of an example data layer.

[0496] FIG.108 is a schematic view of an example data layer.

[0497] FIG.109 is a schematic view of an example intelligent data layer architecture.

[0498] FIG.110 is a schematic view of an example network layer.

[0499] FIG.111 is a schematic view of an example AI subsystem integrator system.

[0500] FIG.112 is a schematic view of an example multiplatform attention management system.

[0501] FIG.113 illustrates an example configured artificial intelligence system.

[0502] FIG.114 illustrates a simplified example of a KYX system.

[0503] FIG.115 is a schematic diagram detailing an example artificial neural network with multiple layers.

[0504] FIG.116 is a schematic diagram detailing an example of training and inference of an example artificial neural network.

[0505] FIG. 117 is a schematic diagram detailing an example of a determination of attention by a machine learning model.

[0506] FIG.118 is a schematic diagram of a first transformer model.

[0507] FIG.119 is a schematic diagram of a second transformer model.

[0508] FIG. 120 is a schematic diagram detailing an example system in which a large language model includes a retrieval component that provides a RAG capability.

[0509] FIG.121 is a schematic diagram detailing an example of a tool use by an example AI agent.

[0510] FIG.122 is a schematic diagram detailing an example AI agent featuring an agent loop.

[0511] FIG. 123 is a schematic diagram detailing a development of an artificial neural network by reinforcement learning.

[0512] FIG. 124 is an illustration of a matrix for organizing and interconnecting various features of AI agent understanding.

[0513] FIG. 125 is a diagram that illustrates an exemplary embodiment of a system of models architecture within the intelligence system of the configured artificial intelligence system.

[0514] FIG.126 is a diagram that illustrates an exemplary embodiment of chipset architectures for systems of models.

[0515] Like reference symbols in the various drawings indicate like elements. DETAILED DESCRIPTION

[0516] The present disclosure will now be described in detail by describing various illustrative, non-limiting embodiments thereof with reference to the accompanying drawings and exhibits. The disclosure may, however, be embodied in many different forms and should not be construed as being limited to the illustrative embodiments set Page 32 of 713SFT-108-A-PCT forth herein. Rather, the embodiments are provided so that this disclosure will be thorough and will fully convey the concept of the disclosure to those skilled in the art. The claims should be consulted to ascertain the true scope of the disclosure.

[0517] Before describing in detail embodiments that are in accordance with the systems and methods disclosed herein, it should be observed that the embodiments reside primarily in combinations of method and / or system components. Accordingly, the system components and methods have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the systems and methods disclosed herein.

[0518] All documents mentioned herein are hereby incorporated by reference in their entirety. References to items in the singular should be understood to include items in the plural, and vice versa, unless explicitly stated otherwise or clear from the context. Grammatical conjunctions are intended to express any and all disjunctive and conjunctive combinations of conjoined clauses, sentences, words, and the like, unless otherwise stated or clear from the context. Thus, the term “or” should generally be understood to mean “and / or” and so forth, except where the context clearly indicates otherwise.

[0519] Recitation of ranges of values herein are not intended to be limiting, referring instead individually to any and all values falling within the range, unless otherwise indicated herein, and each separate value within such a range is incorporated into the specification as if it were individually recited herein. The words “about,” “approximately,” or the like, when accompanying a numerical value, are to be construed as indicating a deviation as would be appreciated by one skilled in the art to operate satisfactorily for an intended purpose. Ranges of values and / or numeric values are provided herein as examples only, and do not constitute a limitation on the scope of the described embodiments. The use of any and all examples, or exemplary language (“e.g.,” “such as,” or the like) provided herein, is intended merely to better illuminate the embodiments and does not pose a limitation on the scope of the embodiments or the claims. No language in the specification should be construed as indicating any unclaimed element as essential to the practice of the embodiments.

[0520] In the following description, it is understood that terms such as “first,” “second,” “third,” “above,” “below,” and the like, are words of convenience and are not to be construed as implying a chronological order or otherwise limiting any corresponding element unless expressly stated otherwise. The term “set” should be understood to encompass a set with a single member or a plurality of members.

[0521] Referring to FIG. 1, an architecture for a transportation system 111 is depicted, showing certain illustrative components and arrangements relating to certain embodiments described herein. The transportation system 111 may include one or more vehicles 110, which may include various mechanical, electrical, and software components and systems, such as a powertrain 113, a suspension system 117, a steering system, a braking system, a fuel system, a charging system, seats 128, a combustion engine, an electric vehicle drive train, a transmission 119, a gear set, and the like. The vehicle may have a vehicle user interface 123, which may include a set of interfaces that include a steering system, buttons, levers, touch screen interfaces, audio interfaces, and the like as described throughout this disclosure. The vehicle may have a set of sensors 125 (including cameras 127), such as for providing input to expert system / artificial intelligence features described throughout this disclosure, such as one or more neural networks (which may include hybrid neural networks 147 as described herein). Sensors 125 and / or external information may be used to inform the expert system / Artificial Intelligence (AI) system 136 and to indicate or track one or more vehicle states 144, such as vehicle operating states 345 (FIG. 3), user experience states 346 (FIG.3), and others described herein, Page 33 of 713SFT-108-A-PCT which also may be as inputs to or taken as outputs from a set of expert system / AI components. Routing information 143 may inform and take input from the expert system / AI system 136, including using in-vehicle navigation capabilities and external navigation capabilities, such as Global Position System (GPS), routing by triangulation (such as cell towers), peer-to-peer routing with other vehicles 121, and the like. A collaboration engine 129 may facilitate collaboration among vehicles and / or among users of vehicles, such as for managing collective experiences, managing fleets and the like. Vehicles 110 may be networked among each other in a peer-to-peer manner, such as using cognitive radio, cellular, wireless or other networking features. An AI system 136 or other expert systems may take as input a wide range of vehicle parameters 130, such as from onboard diagnostic systems, telemetry systems, and other software systems, as well as from vehicle-located sensors 125 and from external systems. In embodiments, the system may manage a set of feedback / rewards 148, incentives, or the like, such as to induce certain user behavior and / or to provide feedback to the AI system 136, such as for learning on a set of outcomes to accomplish a given task or objective. The expert system or AI system 136 may inform, use, manage, or take output from a set of algorithms 149, including a wide variety as described herein. In the example of the present disclosure depicted in FIG.1, a data processing system 162, is connected to the hybrid neural network 147. The data processing system 162 may process data from various sources (see FIG. 7). In the example of the present disclosure depicted in FIG. 1, a system user interface 163, is connected to the hybrid neural network 147. See the disclosure, below, relating to FIG.6 for further disclosure relating to interfaces. FIG. 1 shows that vehicle surroundings 164 may be part of the transportation system 111. Vehicle surroundings may include roadways, weather conditions, lighting conditions, etc. FIG.1 shows that devices 165, for example, mobile phones and computer systems, navigation systems, etc., may be connected to various elements of the transportation system 111, and therefore may be part of the transportation system 111 of the present disclosure.

[0522] Referring to FIG. 2, provided herein are transportation systems having a hybrid neural network 247 for optimizing a powertrain 213 of a vehicle, wherein at least two parts of the hybrid neural network 247 optimize distinct parts of the powertrain 213. An artificial intelligence system may control a powertrain component 215 based on an operational model (such as a physics model, an electrodynamic model, a hydrodynamic model, a chemical model, or the like for energy conversion, as well as a mechanical model for operation of various dynamically interacting system components). For example, the AI system may control a powertrain component 215 by manipulating a powertrain operating parameter 260 to achieve a powertrain state 261. The AI system may be trained to operate a powertrain component 215, such as by training on a data set of outcomes (e.g., fuel efficiency, safety, rider satisfaction, or the like) and / or by training on a data set of operator actions (e.g., driver actions sensed by a sensor set, camera or the like or by a vehicle information system). In embodiments, a hybrid approach may be used, where one neural network optimizes one part of a powertrain (e.g., for gear shifting operations), while another neural network optimizes another part (e.g., braking, clutch engagement, or energy discharge and recharging, among others). Any of the powertrain components described throughout this disclosure may be controlled by a set of control instructions that consist of output from at least one component of a hybrid neural network 247.

[0523] FIG. 3 illustrates a set of states that may be provided as inputs to and / or be governed by an expert system / AI system 336, as well as used in connection with various systems and components in various embodiments described herein. States 344 may include vehicle operating states 345, including vehicle configuration states, component states, diagnostic states, performance states, location states, maintenance states, and many others, as well as user experience states 346, such as experience-specific states, emotional states 366 for users, satisfaction states 367, location states, content / entertainment states and many others. Page 34 of 713SFT-108-A-PCT

[0524] FIG.4 illustrates a range of parameters 430 that may be taken as inputs by an expert system or AI system 136 (FIG.1), or component thereof, as described throughout this disclosure, or that may be provided as outputs from such a system and / or one or more sensors 125 (FIG. 1), cameras 127 (FIG. 1), or external systems. Parameters 430 may include one or more goals 431 or objectives (such as ones that are to be optimized by an expert system / AI system, such as by iteration and / or machine learning), such as a performance goal 433, such as relating to fuel efficiency, trip time, satisfaction, financial efficiency, safety, or the like. Parameters 430 may include market feedback parameters 435, such as relating to pricing, availability, location, or the like of goods, services, fuel, electricity, advertising, content, or the like. Parameters 430 may include rider state parameters 437, such as parameters relating to comfort 439, emotional state, satisfaction, goals, type of trip, fatigue and the like. Parameters 430 may include parameters of various transportation-relevant profiles, such as traffic profiles 440 (location, direction, density and patterns in time, among many others), road profiles 441 (elevation, curvature, direction, road surface conditions and many others), user profiles, and many others. Parameters 430 may include routing parameters 442, such as current vehicle locations, destinations, waypoints, points of interest, type of trip, goal for trip, required arrival time, desired user experience, and many others. Parameters 430 may include satisfaction parameters 443, such as for riders (including drivers), fleet managers, advertisers, merchants, owners, operators, insurers, regulators and others. Parameters 430 may include operating parameters 444, including the wide variety described throughout this disclosure.

[0525] FIG.5 illustrates a set of vehicle user interfaces 523. Vehicle user interfaces 523 may include electromechanical interfaces 568, such as steering interfaces, braking interfaces, interfaces for seats, windows, moonroof, glove box and the like. Interfaces 523 may include various software interfaces (which may have touch screen, dials, knobs, buttons, icons or other features), such as a game interface 569, a navigation interface 570, an entertainment interface 571, a vehicle settings interface 572, a search interface 573, an ecommerce interface 574, and many others. Vehicle interfaces may be used to provide inputs to, and may be governed by, one or more AI systems / expert systems such as described in embodiments throughout this disclosure.

[0526] FIG.6 illustrates a set of interfaces among transportation system components, including interfaces within a host system (such as governing a vehicle or fleet of vehicles) and host interfaces 650 between a host system and one or more third parties and / or external systems. Interfaces include third party interfaces 655 and end user interfaces 651 for users of the host system, including the in-vehicle interfaces that may be used by riders as noted in connection with FIG.5, as well as user interfaces for others, such as fleet managers, insurers, regulators, police, advertisers, merchants, content providers, and many others. Interfaces may include merchant interfaces 652, by which merchants may provide advertisements, content relating to offerings, and one or more rewards, such as to induce routing or other behavior on the part of users. Interfaces may include machine interfaces 653, such as application programming interfaces (API) 654, networking interfaces, peer-to-peer interfaces, connectors, brokers, extract-transform-load (ETL) system, bridges, gateways, ports and the like. Interfaces may include one or more host interfaces by which a host may manage and / or configure one or more of the many embodiments described herein, such as configuring neural network components, setting weight for models, setting one or more goals or objectives, setting reward parameters 656, and many others. Interfaces may include expert system / AI system configuration interfaces 657, such as for selecting one or more models 658, selecting and configuring data sets 659 (such as sensor data, external data and other inputs described herein), AI selection 660 and AI configuration 661 (such as selection of neural network category, parameter weighting and the like), feedback selection 662 for an expert system / AI system, such as for learning, and supervision configuration 663, among many others. Page 35 of 713SFT-108-A-PCT

[0527] FIG.7 illustrates a data processing system 758, which may process data from various sources, including social media data sources 769, weather data sources 770, road profile sources 771, traffic data sources 772, media data sources 773, sensors sets 774, and many others. The data processing system may be configured to extract data, transform data to a suitable format (such as for use by an interface system, an AI system / expert system, or other systems), load it to an appropriate location, normalize data, cleanse data, deduplicate data, store data (such as to enable queries) and perform a wide range of processing tasks as described throughout this disclosure.

[0528] FIG. 8 illustrates a set of algorithms 849 that may be executed in connection with one or more of the many embodiments of transportation systems described throughout this disclosure. Algorithms 849 may take input from, provide output to, and be managed by a set of AI systems / expert systems, such as of the many types described herein. Algorithms 849 may include algorithms for providing or managing user satisfaction 874, one or more genetic algorithms 875, such as for seeking favorable states, parameters, or combinations of states / parameters in connection with optimization of one or more of the systems described herein. Algorithms 849 may include vehicle routing algorithms 876, including ones that are sensitive to various vehicle operating parameters, user experience parameters, or other states, parameters, profiles, or the like described herein, as well as to various goals or objectives. Algorithms 849 may include object detection algorithms 881. Algorithms 849 may include energy calculation algorithms 877, such as for calculating energy parameters, for optimizing fuel usage, electricity usage or the like, for optimizing refueling or recharging time, location, amount or the like. Algorithms may include prediction algorithms 878, such as for a traffic prediction algorithm 879, a transportation prediction algorithm 880, and algorithms for predicting other states or parameters of transportation systems as described throughout this disclosure.

[0529] In various embodiments, transportation systems 111 as described herein may include vehicles (including fleets and other sets of vehicles), as well as various infrastructure systems. Infrastructure systems may include Internet of Things systems (such as using cameras and other sensors, such as disposed on or in roadways, on or in traffic lights, utility poles, toll booths, signs and other roadside devices and systems, on or in buildings, and the like), refueling and recharging systems (such as at service stations, charging locations and the like, and including wireless recharging systems that use wireless power transfer), and many others.

[0530] Vehicle electrical, mechanical and / or powertrain components as described herein may include a wide range of systems, including transmission, gear system, clutch system, braking system, fuel system, lubrication system, steering system, suspension system, lighting system (including emergency lighting as well as interior and exterior lights), electrical system, and various subsystems and components thereof.

[0531] Vehicle operating states and parameters may include route, purpose of trip, geolocation, orientation, vehicle range, powertrain parameters, current gear, speed / acceleration, suspension profile (including various parameters, such as for each wheel), charge state for electric and hybrid vehicles, fuel state for fueled vehicles, and many others as described throughout this disclosure.

[0532] Rider and / or user experience states and parameters as described throughout this disclosure may include emotional states, comfort states, psychological states (e.g., anxiety, nervousness, relaxation or the like), awake / asleep states, and / or states related to satisfaction, alertness, health, wellness, one or more goals or objectives, and many others. User experience parameters as described herein may further include ones related to driving, braking, curve approach, seat positioning, window state, ventilation system, climate control, temperature, humidity, sound level, entertainment content type (e.g., news, music, sports, comedy, or the like), route selection (such as for POIs, scenic views, new sites and the like), and many others. Page 36 of 713SFT-108-A-PCT

[0533] In embodiments, a route may be ascribed various parameters of value, such as parameters of value that may be optimized to improve user experience or other factors, such as under control of an AI system / expert system. Parameters of value of a route may include speed, duration, on time arrival, length (e.g., in miles), goals (e.g., to see a Point of Interest (POI), to complete a task (e.g., complete a shopping list, complete a delivery schedule, complete a meeting, or the like), refueling or recharging parameters, game-based goals, and others. As one of many examples, a route may be attributed value, such as in a model and / or as an input or feedback to an AI system or expert system that is configured to optimize a route, for task completion. A user may, for example, indicate a goal to meet up with at least one of a set of friends during a weekend, such as by interacting with a user interface or menu that allows setting of objectives. A route may be configured (including with inputs that provide awareness of friend locations, such as by interacting with systems that include location information for other vehicles and / or awareness of social relationships, such as through social data feeds) to increase the likelihood of meeting up, such as by intersecting with predicted locations of friends (which may be predicted by a neural network or other AI system / expert system as described throughout this disclosure) and by providing in-vehicle messages (or messages to a mobile device) that indicates possible opportunities for meeting up.

[0534] Market feedback factors may be used to optimize various elements of transportation systems as described throughout this disclosure, such as current and predicted pricing and / or cost (e.g., of fuel, electricity and the like, as well as of goods, services, content and the like that may be available along the route and / or in a vehicle), current and predicted capacity, supply and / or demand for one or more transportation related factors (such as fuel, electricity, charging capacity, maintenance, service, replacement parts, new or used vehicles, capacity to provide ride sharing, self-driving vehicle capacity or availability, and the like), and many others.

[0535] An interface in or on a vehicle may include a negotiation system, such as a bidding system, a price-negotiating system, a reward-negotiating system, or the like. For example, a user may negotiate for a higher reward in exchange for agreeing to re-route to a merchant location, a user may name a price the user is willing to pay for fuel (which may be provided to nearby refueling stations that may offer to meet the price), or the like. Outputs from negotiation (such as agreed prices, trips and the like) may automatically result in reconfiguration of a route, such as one governed by an AI system / expert system.

[0536] Rewards, such as provided by a merchant or a host, among others, as described herein may include one or more coupons, such as redeemable at a location, provision of higher priority (such as in collective routing of multiple vehicles), permission to use a “Fast Lane,” priority for charging or refueling capacity, among many others. Actions that can lead to rewards in a vehicle may include playing a game, downloading an app, driving to a location, taking a photograph of a location or object, visiting a website, viewing or listening to an advertisement, watching a video, and many others.

[0537] In embodiments, an AI system / expert system may use or optimize one or more parameters for a charging plan, such as for charging a battery of an electric or hybrid vehicle. Charging plan parameters may include routing (such as to charging locations), amount of charge or fuel provided, duration of time for charging, battery state, battery charging profile, time required to charge, value of charging, indicators of value, market price, bids for charging, available supply capacity (such as within a geofence or within a range of a set of vehicles), demand (such as based on detected charge / refueling state, based on requested demand, or the like), supply, and others. A neural network or other systems (optionally a hybrid system as described herein), using a model or algorithm (such as a genetic algorithm) may be used (such as by being trained over a set of trials on outcomes, and / or using a training set of human created or human Page 37 of 713SFT-108-A-PCT supervised inputs, or the like) may provide a favorable and / or optimized charging plan for a vehicle or a set of vehicles based on the parameters. Other inputs may include priority for certain vehicles (e.g., for emergency responders or for those who have been rewarded priority in connection with various embodiments described herein).

[0538] In embodiments, a processor, as described herein, may comprise a neural processing chip, such as one employing a fabric, such as a LambdaFabric. Such a chip may have a plurality of cores, such as 256 cores, where each core is configured in a neuron-like arrangement with other cores on the same chip. Each core may comprise a micro-scale digital signal processor, and the fabric may enable the cores to readily connect to the other cores on the chip. In embodiments, the fabric may connect a large number of cores (e.g., more than 500,000 cores) and / or chips, thereby facilitating use in computational environments that require, for example, large scale neural networks, massively parallel computing, and large-scale, complex conditional logic. In embodiments, a low-latency fabric is used, such as one that has latency of 400 nanoseconds, 300 nanoseconds, 200 nanoseconds, 100 nanoseconds, or less from device- to-device, rack-to-rack, or the like. The chip may be a low power chip, such as one that can be powered by energy harvesting from the environment, from an inspection signal, from an onboard antenna, or the like. In embodiments, the cores may be configured to enable application of a set of sparse matrix heterogeneous machine learning algorithms. The chip may run an object-oriented programming language, such as C++, Java, or the like. In embodiments, a chip may be programmed to run each core with a different algorithm, thereby enabling heterogeneity in algorithms, such as to enable one or more of the hybrid neural network embodiments described throughout this disclosure. A chip can thereby take multiple inputs (e.g., one per core) from multiple data sources, undertake massively parallel processing using a large set of distinct algorithms, and provide a plurality of outputs (such as one per core or per set of cores).

[0539] In embodiments, a chip may contain or enable a security fabric, such as a fabric for performing content inspection, packet inspection (such as against a black list, white list, or the like), and the like, in addition to undertaking processing tasks, such as for a neural network, hybrid AI solution, or the like.

[0540] In embodiments, the platform described herein may include, integrate with, or connect with a system for robotic process automation (RPA), whereby an artificial intelligence / machine learning system may be trained on a training set of data that consists of tracking and recording sets of interactions of humans as the humans interact with a set of interfaces, such as graphical user interfaces (e.g., via interactions with mouse, trackpad, keyboard, touch screen, joystick, remote control devices); audio system interfaces (such as by microphones, smart speakers, voice response interfaces, intelligent agent interfaces (e.g., Siri and Alexa) and the like); human-machine interfaces (such as involving robotic systems, prosthetics, cybernetic systems, exoskeleton systems, wearables (including clothing, headgear, headphones, watches, wrist bands, glasses, arm bands, torso bands, belts, rings, necklaces and other accessories); physical or mechanical interfaces (e.g., buttons, dials, toggles, knobs, touch screens, levers, handles, steering systems, wheels, and many others); optical interfaces (including ones triggered by eye tracking, facial recognition, gesture recognition, emotion recognition, and the like); sensor-enabled interfaces (such as ones involving cameras, EEG or other electrical signal sensing (such as for brain-computer interfaces), magnetic sensing, accelerometers, galvanic skin response sensors, optical sensors, IR sensors, LIDAR and other sensor sets that are capable of recognizing thoughts, gestures (facial, hand, posture, or other), utterances, and the like, and others. In addition to tracking and recording human interactions, the RPA system may also track and record a set of states, actions, events and results that occur by, within, from or about the systems and processes with which the humans are engaging. For example, the RPA system may record mouse clicks on a frame of video that appears within a process by which a human review the video, such as where the human highlights points of interest within the video, tags objects in the video, captures parameters (such Page 38 of 713SFT-108-A-PCT as sizes, dimensions, or the like), or otherwise operates on the video within a graphical user interface. The RPA system may also record system or process states and events, such as recording what elements were the subject of interaction, what the state of a system was before, during and after interaction, and what outputs were provided by the system or what results were achieved. Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision, such as by having a human correct the RPA system as it attempts in a set of trials to undertake the action that the human would have undertaken (e.g., tagging the right object, labeling an item correctly, selecting the correct button to trigger a next step in a process, or the like), such that over a set of trials the RPA system becomes increasingly effective at replicating the action the human would have taken. Learning may include deep learning, such as by reinforcing learning based on outcomes, such as successful outcomes (such as based on successful process completion, financial yield, and many other outcome measures described throughout this disclosure). In embodiments, an RPA system may be seeded during a learning phase with a set of expert human interactions, such that the RPA system begins to be able to replicate expert interaction with a system. For example, an expert driver's interactions with a robotic system, such as a remote-controlled vehicle or a UAV, may be recorded along with information about the vehicles state (e.g., the surrounding environment, navigation parameters, and purpose), such that the RPA system may learn to drive the vehicle in a way that reflects the same choices as an expert driver. After being taught to replicate the skills or expertise of an expert human, the RPA system may be transitioned to a deep learning mode, where the system further improves based on a set of outcomes, such as by being configured to attempt some level of variation in approach (e.g., trying different navigation paths to optimize time of arrival, or trying different approaches to deceleration and acceleration in curves) and tracking outcomes (with feedback), such that the RPA system can learn, by variation / experimentation (which may be randomized, rule-based, or the like, such as using genetic programming techniques, random-walk techniques, random forest techniques, and others) and selection, to exceed the expertise of the human expert. Thus, the RPA system learns from a human expert, acquires expertise in interacting with a system or process, facilitates automation of the process (such as by taking over some of the more repetitive tasks, including ones that require consistent execution of acquired skills), and provides a very effective seed for artificial intelligence, such as by providing a seed model or system that can be improved by machine learning with feedback on outcomes of a system or process.

[0541] RPA systems may have particular value in situations where human expertise or knowledge is acquired with training and experience, as well as in situations where the human brain and sensory systems are particularly adapted and evolved to solve problems that are computationally difficult or highly complex. Thus, in embodiments, RPA systems may be used to learn to undertake, among other things: visual pattern recognition tasks with respect to the various systems, processes, workflows and environments described herein (such as recognizing the meaning of dynamic interactions of objects or entities within a video stream (e.g., to understand what is taking place as humans and objects interact in a video); recognition of the significance of visual patterns (e.g., recognizing objects, structures, defects and conditions in a photograph or radiography image); tagging of relevant objects within a visual pattern (e.g., tagging or labeling objects by type, category, or specific identity (such as person recognition); indication of metrics in a visual pattern (such as dimensions of objects indicated by clicking on dimensions in an x-ray or the like); labeling activities in a visual pattern by category (e.g., what work process is being done); recognizing a pattern that is displayed as a signal (e.g., a wave or similar pattern in a frequency domain, time domain, or other signal processing representation); anticipate a n future state based on a current state (e.g., anticipating motion of a flying or rolling object, Page 39 of 713SFT-108-A-PCT anticipating a next action by a human in a process, anticipating a next step by a machine, anticipating a reaction by a person to an event, and many others); recognize and predicting emotional states and reactions (such as based on facial expression, posture, body language or the like); apply a heuristic to achieve a favorable state without deterministic calculation (e.g., selecting a favorable strategy in sport or game, selecting a business strategy, selecting a negotiating strategy, setting a price for a product, developing a message to promote a product or idea, generating creative content, recognizing a favorable style or fashion, and many others); and many others. In embodiments, an RPA system may automate workflows that involve visual inspection of people, systems, and objects (including internal components), workflows that involve performing software tasks, such as involving sequential interactions with a series of screens in a software interface, workflows that involve remote control of robots and other systems and devices, workflows that involve content creation (such as selecting, editing and sequencing content), workflows that involve financial decision- making and negotiation (such as setting prices and other terms and conditions of financial and other transactions), workflows that involve decision-making (such as selecting an optimal configuration for a system or sub-system, selecting an optimal path or sequence of actions in a workflow, process or other activity that involves dynamic decision-making), and many others.

[0542] In embodiments, an RPA system may use a set of IoT devices and systems (such as cameras and sensors), to track and record human actions and interactions with respect to various interfaces and systems in an environment. The RPA system may also use data from onboard sensors, telemetry, and event recording systems, such as telemetry systems on vehicles and event logs on computers). The RPA system may thus generate and / or receive a large data set (optionally distributed) for an environment (such as any of the environments described throughout this disclosure) including data recording the various entities (human and non-human), systems, processes, applications (e.g., software applications used to enable workflows), states, events, and outcomes, which can be used to train the RPA system (or a set of RPA systems dedicated to automating various processes and workflows) to accomplish processes and workflows in a way that reflects and mimics accumulated human expertise, and that eventually improves on the results of that human expertise by further machine learning.

[0543] Referring to FIG. 9, in embodiments provided herein are systems for transportation 911 having an artificial intelligence system 936 that uses at least one genetic algorithm 975 to explore a set of possible vehicle operating states 945 to determine at least one optimized operating state. In embodiments, the genetic algorithm 975 takes inputs relating to at least one vehicle performance parameter 982 and at least one rider state 937.

[0544] An aspect provided herein includes a system for transportation 911, comprising: a vehicle 910 having a vehicle operating state 945; an artificial intelligence system 936 to execute a genetic algorithm 975 to generate mutations from an initial vehicle operating state to determine at least one optimized vehicle operating state. In embodiments, the vehicle operating state 945 includes a set of vehicle parameter values 984. In embodiments, the genetic algorithm 975 is to: vary the set of vehicle parameter values 984 for a set of corresponding time periods such that the vehicle 910 operates according to the set of vehicle parameter values 984 during the corresponding time periods; evaluate the vehicle operating state 945 for each of the corresponding time periods according to a set of measures 983 to generate evaluations; and select, for future operation of the vehicle 910, an optimized set of vehicle parameter values based on the evaluations.

[0545] In embodiments, the vehicle operating state 945 includes the rider state 937 of a rider of the vehicle. In embodiments, the at least one optimized vehicle operating state includes an optimized state of the rider. In Page 40 of 713SFT-108-A-PCT embodiments, the genetic algorithm 975 is to optimize the state of the rider. In embodiments, the evaluating according to the set of measures 983 is to determine the state of the rider corresponding to the vehicle parameter values 984.

[0546] In embodiments, the vehicle operating state 945 includes a state of the rider of the vehicle. In embodiments, the set of vehicle parameter values 984 includes a set of vehicle performance control values. In embodiments, the at least one optimized vehicle operating state includes an optimized state of performance of the vehicle. In embodiments, the genetic algorithm 975 is to optimize the state of the rider and the state of performance of the vehicle. In embodiments, the evaluating according to the set of measures 983 is to determine the state of the rider and the state of performance of the vehicle corresponding to the vehicle performance control values.

[0547] In embodiments, the set of vehicle parameter values 984 includes a set of vehicle performance control values. In embodiments, the at least one optimized vehicle operating state includes an optimized state of performance of the vehicle. In embodiments, the genetic algorithm 975 is to optimize the state of performance of the vehicle. In embodiments, the evaluating according to the set of measures 983 is to determine the state of performance of the vehicle corresponding to the vehicle performance control values.

[0548] In embodiments, the set of vehicle parameter values 984 includes a rider-occupied parameter value. In embodiments, the rider-occupied parameter value affirms a presence of a rider in the vehicle 910. In embodiments, the vehicle operating state 945 includes the rider state 937 of a rider of the vehicle. In embodiments, the at least one optimized vehicle operating state includes an optimized state of the rider. In embodiments, the genetic algorithm 975 is to optimize the state of the rider. In embodiments, the evaluating according to the set of measures 983 is to determine the state of the rider corresponding to the vehicle parameter values 984. In embodiments, the state of the rider includes a rider satisfaction parameter. In embodiments, the state of the rider includes an input representative of the rider. In embodiments, the input representative of the rider is selected from the group consisting of: a rider state parameter, a rider comfort parameter, a rider emotional state parameter, a rider satisfaction parameter, a rider goals parameter, a classification of the trip, and combinations thereof.

[0549] In embodiments, the set of vehicle parameter values 984 includes a set of vehicle performance control values. In embodiments, the at least one optimized vehicle operating state includes an optimized state of performance of the vehicle. In embodiments, the genetic algorithm 975 is to optimize the state of the rider and the state of performance of the vehicle. In embodiments, the evaluating according to the set of measures 983 is to determine the state of the rider and the state of performance of the vehicle corresponding to the vehicle performance control values. In embodiments, the set of vehicle parameter values 984 includes a set of vehicle performance control values. In embodiments, the at least one optimized vehicle operating state includes an optimized state of performance of the vehicle. In embodiments, the genetic algorithm 975 is to optimize the state of performance of the vehicle. In embodiments, the evaluating according to the set of measures 983 is to determine the state of performance of the vehicle corresponding to the vehicle performance control values.

[0550] In embodiments, the set of vehicle performance control values are selected from the group consisting of: a fuel efficiency; a trip duration; a vehicle wear; a vehicle make; a vehicle model; a vehicle energy consumption profiles; a fuel capacity; a real-time fuel level; a charge capacity; a recharging capability; a regenerative braking state; and combinations thereof. In embodiments, at least a portion of the set of vehicle performance control values is sourced from at least one of an on-board diagnostic system, a telemetry system, a software system, a vehicle-located sensor, and a system external to the vehicle 910. In embodiments, the set of measures 983 relates to a set of vehicle operating criteria. In embodiments, the set of measures 983 relates to a set of rider satisfaction criteria. In embodiments, the set Page 41 of 713SFT-108-A-PCT of measures 983 relates to a combination of vehicle operating criteria and rider satisfaction criteria. In embodiments, each evaluation uses feedback indicative of an effect on at least one of a state of performance of the vehicle and a state of the rider.

[0551] An aspect provided herein includes a system for transportation 911, comprising: an artificial intelligence system 936 to process inputs representative of a state of a vehicle and inputs representative of a rider state 937 of a rider occupying the vehicle during the state of the vehicle with the genetic algorithm 975 to optimize a set of vehicle parameters that affects the state of the vehicle or the rider state 937. In embodiments, the genetic algorithm 975 is to perform a series of evaluations using variations of the inputs. In embodiments, each evaluation in the series of evaluations uses feedback indicative of an effect on at least one of a vehicle operating state 945 and the rider state 937. In embodiments, the inputs representative of the rider state 937 indicate that the rider is absent from the vehicle 910. In embodiments, the state of the vehicle includes the vehicle operating state 945. In embodiments, a vehicle parameter in the set of vehicle parameters includes a vehicle performance parameter 982. In embodiments, the genetic algorithm 975 is to optimize the set of vehicle parameters for the state of the rider.

[0552] In embodiments, optimizing the set of vehicle parameters is responsive to an identifying, by the genetic algorithm 975, of at least one vehicle parameter that produces a favorable rider state. In embodiments, the genetic algorithm 975 is to optimize the set of vehicle parameters for vehicle performance. In embodiments, the genetic algorithm 975 can optimize the set of vehicle parameters for the state of the rider and can optimize the set of vehicle parameters for vehicle performance. In embodiments, optimizing the set of vehicle parameters is responsive to the genetic algorithm 975 identifying at least one of a favorable vehicle operating state, and favorable vehicle performance that maintains the rider state 937. In embodiments, the artificial intelligence system 936 further includes a neural network selected from a plurality of different neural networks. In embodiments, the selection of the neural network involves the genetic algorithm 975. In embodiments, the selection of the neural network is based on a structured competition among the plurality of different neural networks. In embodiments, the genetic algorithm 975 facilitates training a neural network to process interactions among a plurality of vehicle operating systems and riders to produce the optimized set of vehicle parameters.

[0553] In embodiments, a set of inputs relating to at least one vehicle parameter are provided by at least one of an on- board diagnostic system, a telemetry system, a vehicle-located sensor, and a system external to the vehicle. In embodiments, the inputs representative of the rider state 937 comprise at least one of comfort, emotional state, satisfaction, goals, classification of trip, or fatigue. In embodiments, the inputs representative of the rider state 937 reflect a satisfaction parameter of at least one of a driver, a fleet manager, an advertiser, a merchant, an owner, an operator, an insurer, and a regulator. In embodiments, the inputs representative of the rider state 937 comprise inputs relating to a user that, when processed with a cognitive system yield the rider state 937.

[0554] Referring to FIG.10, in embodiments provided herein are systems for transportation 1011 having a hybrid neural network 1047 for optimizing the operating state of a continuously variable powertrain 1013 of a vehicle 1010. In embodiments, at least one part of the hybrid neural network 1047 operates to classify a state of the vehicle 1010 and another part of the hybrid neural network 1047 operates to optimize at least one operating parameter 1087 of the transmission 1019. In embodiments, the vehicle 1010 may be a self-driving vehicle. In an example, the first portion 1085 of the hybrid neural network may classify the vehicle 1010 as operating in a high-traffic state (such as by use of LIDAR, RADAR, or the like that indicates the presence of other vehicles, or by taking input from a traffic monitoring system, or by detecting the presence of a high density of mobile devices, or the like) and a bad weather state (such as Page 42 of 713SFT-108-A-PCT by taking inputs indicating wet roads (such as using vision-based systems), precipitation (such as determined by radar), presence of ice (such as by temperature sensing, vision-based sensing, or the like), hail (such as by impact detection, sound-sensing, or the like), lightning (such as by vision-based systems, sound-based systems, or the like), or the like. Once classified, another neural network 11386 (optionally of another type) may optimize the vehicle operating parameter based on the classified state, such as by putting the vehicle 1010 into a safe-driving mode (e.g., by providing forward-sensing alerts at greater distances and / lower speeds than in good weather, by providing automated braking earlier and more aggressively than in good weather, and the like).

[0555] An aspect provided herein includes a system for transportation 1011, comprising: a hybrid neural network 1047 for optimizing an operating state of a continuously variable powertrain 1013 of a vehicle 1010. In embodiments, a portion 1085 of the hybrid neural network 1047 is to operate to classify a state 1044 of the vehicle 1010 thereby generating a classified state of the vehicle, and another neural network 11386 portion of the hybrid neural network 1047 is to operate to optimize at least one operating parameter 1060 of a transmission 1019 portion of the continuously variable powertrain 1013.

[0556] In embodiments, the system for transportation 1011 further comprises: an artificial intelligence system 1036 operative on at least one processor 11388, the artificial intelligence system 1036 to operate the portion 1085 of the hybrid neural network 1047 to operate to classify the state of the vehicle and the artificial intelligence system 1036 to operate the other neural network 11386 portion of the hybrid neural network 1047 to optimize the at least one operating parameter 1087 of the transmission 1019 portion of the continuously variable powertrain 1013 based on the classified state of the vehicle. In embodiments, the vehicle 1010 comprises a system for automating at least one control parameter of the vehicle. In embodiments, the vehicle 1010 is at least a semi-autonomous vehicle. In embodiments, the vehicle 1010 is to be automatically routed. In embodiments, the vehicle 1010 is a self-driving vehicle. In embodiments, the classified state of the vehicle is: a vehicle maintenance state; a vehicle health state; a vehicle operating state; a vehicle energy utilization state; a vehicle charging state; a vehicle satisfaction state; a vehicle component state; a vehicle sub- system state; a vehicle powertrain system state; a vehicle braking system state; a vehicle clutch system state; a vehicle lubrication system state; a vehicle transportation infrastructure system state; or a vehicle rider state. In embodiments, at least a portion of the hybrid neural network 1047 is a convolutional neural network.

[0557] FIG. 11 illustrates a method 1100 for optimizing operation of a continuously variable vehicle powertrain of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At 1102, the method includes executing a first network of a hybrid neural network on at least one processor, the first network classifying a plurality of operational states of the vehicle. In embodiments, at least a portion of the operational states is based on a state of the continuously variable powertrain of the vehicle. At 1104, the method includes executing a second network of the hybrid neural network on the at least one processor, the second network processing inputs that are descriptive of the vehicle and of at least one detected condition associated with an occupant of the vehicle for at least one of the plurality of classified operational states of the vehicle. In embodiments, the processing of the inputs by the second network can cause optimization of at least one operating parameter of the continuously variable powertrain of the vehicle for a plurality of the operational states of the vehicle.

[0558] Referring to FIG. 10 and FIG. 11 together, in embodiments, the vehicle comprises an artificial intelligence system 1036, the method further comprising automating at least one control parameter of the vehicle by the artificial intelligence system 1036. In embodiments, the vehicle 1010 is at least a semi-autonomous vehicle. In embodiments, the vehicle 1010 is to be automatically routed. In embodiments, the vehicle 1010 is a self-driving vehicle. In Page 43 of 713SFT-108-A-PCT embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, an operating state of the continuously variable powertrain 1013 of the vehicle based on the optimized at least one operating parameter 1060 of the continuously variable powertrain 1013 by adjusting at least one other operating parameter 1087 of a transmission 1019 portion of the continuously variable powertrain 1013.

[0559] In embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, the operating state of the continuously variable powertrain 1013 by processing social data from a plurality of social data sources. In embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, the operating state of the continuously variable powertrain 1013 by processing data sourced from a stream of data from unstructured data sources. In embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, the operating state of the continuously variable powertrain 1013 by processing data sourced from wearable devices. In embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, the operating state of the continuously variable powertrain 1013 by processing data sourced from in-vehicle sensors. In embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, the operating state of the continuously variable powertrain 1013 by processing data sourced from a rider helmet.

[0560] In embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, the operating state of the continuously variable powertrain 1013 by processing data sourced from rider headgear. In embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, the operating state of the continuously variable powertrain 1013 by processing data sourced from a rider voice system. In embodiments, the method further comprises operating, by the artificial intelligence system 1036, a third network of the hybrid neural network 1047 to predict a state of the vehicle based at least in part on at least one of the classified plurality of operational states of the vehicle and at least one operating parameter of the transmission 1019. In embodiments, the first network of the hybrid neural network 1047 comprises a structure-adaptive network to adapt a structure of the first network responsive to a result of operating the first network of the hybrid neural network 1047. In embodiments, the first network of the hybrid neural network 1047 is to process a plurality of social data from social data sources to classify the plurality of operational states of the vehicle.

[0561] In embodiments, at least a portion of the hybrid neural network 1047 is a convolutional neural network. In embodiments, at least one of the classified plurality of operational states of the vehicle is: a vehicle maintenance state; or a vehicle health state. In embodiments, at least one of the classified states of the vehicle is: a vehicle operating state; a vehicle energy utilization state; a vehicle charging state; a vehicle satisfaction state; a vehicle component state; a vehicle sub-system state; a vehicle powertrain system state; a vehicle braking system state; a vehicle clutch system state; a vehicle lubrication system state; or a vehicle transportation infrastructure system state. In embodiments, the at least one of classified states of the vehicle is a vehicle driver state. In embodiments, the at least one of classified states of the vehicle is a vehicle rider state.

[0562] Referring to FIG. 12, in embodiments, provided herein are transportation systems 1211 having a cognitive system for routing at least one vehicle 1210 within a set of vehicles 1294 based on a routing parameter determined by facilitating negotiation among a designated set of vehicles. In embodiments, negotiation accepts inputs relating to the value attributed by at least one rider to at least one parameter 1230 of a route 1295. A user 1290 may express value by a user interface that rates one or more parameters (e.g., any of the parameters noted throughout), by behavior (e.g., undertaking behavior that reflects or indicates value ascribed to arriving on time, following a given route 1295, or the like), or by providing or offering value (e.g., offering currency, tokens, points, cryptocurrency, rewards, or the like). Page 44 of 713SFT-108-A-PCT For example, a user 1290 may negotiate for a preferred route by offering tokens to the system that are awarded if the user 1290 arrives at a designated time, while others may offer to accept tokens in exchange for taking alternative routes (and thereby reducing congestion). Thus, an artificial intelligence system may optimize a combination of offers to provide rewards or to undertake behavior in response to rewards, such that the reward system optimizes a set of outcomes. Negotiation may include explicit negotiation, such as where a driver offers to reward drivers ahead of the driver on the road in exchange for their leaving the route temporarily as the driver passes.

[0563] An aspect provided herein includes a transportation system 1211, comprising: a cognitive system for routing at least one vehicle 1210 within a set of vehicles 1294 based on a routing parameter determined by facilitating a negotiation among a designated set of vehicles, wherein the negotiation accepts inputs relating to a value attributed by at least one user 1290 to at least one parameter of a route 1295.

[0564] FIG.13 illustrates a method 1300 of negotiation-based vehicle routing in accordance with embodiments of the systems and methods disclosed herein. At 1302, the method includes facilitating a negotiation of a route-adjustment value for a plurality of parameters used by a vehicle routing system to route at least one vehicle in a set of vehicles. At 1304, the method includes determining a parameter in the plurality of parameters for optimizing at least one outcome based on the negotiation.

[0565] Referring to FIG.12 and FIG.13, in embodiments, a user 1290 is an administrator for a set of roadways to be used by the at least one vehicle 1210 in the set of vehicles 1294. In embodiments, a user 1290 is an administrator for a fleet of vehicles including the set of vehicles 1294. In embodiments, the method further comprises offering a set of offered user-indicated values for the plurality of parameters 1230 to users 1290 with respect to the set of vehicles 1294. In embodiments, the route-adjustment value 1224 is based at least in part on the set of offered user-indicated values 1297. In embodiments, the route-adjustment value 1224 is further based on at least one user response to the offering. In embodiments, the route-adjustment value 1224 is based at least in part on the set of offered user-indicated values 1297 and at least one response thereto by at least one user of the set of vehicles 1294. In embodiments, the determined parameter facilitates adjusting a route 1295 of at least one of the vehicles 1210 in the set of vehicles 1294. In embodiments, adjusting the route includes prioritizing the determined parameter for use by the vehicle routing system.

[0566] In embodiments, the facilitating negotiation includes facilitating negotiation of a price of a service. In embodiments, the facilitating negotiation includes facilitating negotiation of a price of fuel. In embodiments, the facilitating negotiation includes facilitating negotiation of a price of recharging. In embodiments, the facilitating negotiation includes facilitating negotiation of a reward for taking a routing action.

[0567] An aspect provided herein includes a transportation system 1211 for negotiation-based vehicle routing comprising: a route adjustment negotiation system 1236 through which users 1290 in a set of users 1291 negotiate a route-adjustment value 1224 for at least one of a plurality of parameters 1230 used by a vehicle routing system 1292 to route at least one vehicle 1210 in a set of vehicles 1294; and a user route optimizing circuit 1245 to optimize a portion of a route 1295 of at least one user 1290 of the set of vehicles 1294 based on the route-adjustment value 1224 for the at least one of the plurality of parameters 1230. In embodiments, the route-adjustment value 1224 is based at least in part on user-indicated values 1297 and at least one negotiation response thereto by at least one user of the set of vehicles 1294. In embodiments, the transportation system 1211 further comprises a vehicle-based route negotiation interface 1296 through which user-indicated values 1297 for the plurality of parameters 1230 used by the vehicle routing system are captured. In embodiments, a user 1290 is a rider of the at least one vehicle 1210. In embodiments, Page 45 of 713SFT-108-A-PCT a user 1290 is an administrator for a set of roadways to be used by the at least one vehicle 1210 in the set of vehicles 1294.

[0568] In embodiments, a user 1290 is an administrator for a fleet of vehicles including the set of vehicles 1294. In embodiments, the at least one of the plurality of parameters 1230 facilitates adjusting a route 1295 of the at least one vehicle 1210. In embodiments, adjusting the route 1295 includes prioritizing a determined parameter for use by the vehicle routing system. In embodiments, at least one of the user-indicated values 1297 is attributed to at least one of the plurality of parameters 1230 through an interface to facilitate expression of rating one or more route parameters. In embodiments, the vehicle-based route negotiation interface facilitates expression of rating one or more route parameters. In embodiments, the user-indicated values 1297 are derived from a behavior of the user 1290. In embodiments, the vehicle-based route negotiation interface facilitates converting user behavior to the user-indicated values 1297. In embodiments, the user behavior reflects value ascribed to the at least one parameter used by the vehicle routing system to influence a route 1295 of at least one vehicle 1210 in the set of vehicles 1294. In embodiments, the user-indicated value indicated by at least one user 1290 correlates to an item of value provided by the user 1290. In embodiments, the item of value is provided by the user 1290 through an offering of the item of value in exchange for a result of routing based on the at least one parameter. In embodiments, the negotiating of the route-adjustment value 1224 includes offering an item of value to the users of the set of vehicles 1294.

[0569] Referring to FIG.14, in embodiments provided herein are transportation systems 1411 having a cognitive system for routing at least one vehicle 1410 within a set of vehicles 1494 based on a routing parameter determined by facilitating coordination among a designated set of vehicles 1498. In embodiments, the coordination is accomplished by taking at least one input from at least one game-based interface 1499 for riders of the vehicles. A game-based interface 1499 may include rewards for undertaking game-like actions (i.e., game activities 14101) that provide an ancillary benefit. For example, a rider in a vehicle 1410 may be rewarded for routing the vehicle 1410 to a point of interest off a highway (such as to collect a coin, to capture an item, or the like), while the rider’s departure clears space for other vehicles that are seeking to achieve other objectives, such as on-time arrival. For example, a game like Pokemon Go™ may be configured to indicate the presence of rare Pokemon™ creatures in locations that attract traffic away from congested locations. Others may provide rewards (e.g., currency, cryptocurrency or the like) that may be pooled to attract users 1490 away from congested roads.

[0570] An aspect provided herein includes a transportation system 1411, comprising: a cognitive system for routing at least one vehicle 1410 within a set of vehicles 1494 based on a set of routing parameters 1430 determined by facilitating coordination among a designated set of vehicles 1498, wherein the coordination is accomplished by taking at least one input from at least one game-based interface 1499 for a user 1490 of users 1491 of a vehicle 1410 in the designated set of vehicles 1498.

[0571] In embodiments, the system for transportation further comprises: a vehicle routing system 1492 to route the at least one vehicle 1410 based on the set of routing parameters 1430; and the game-based interface 1499 through which the user 1490 indicates a routing preference 14100 for at least one vehicle 1410 within the set of vehicles 1494 to undertake a game activity 14101 offered in the game-based interface 1499; wherein the game-based interface 1499 is to induce the user 1490 to undertake a set of favorable routing choices based on the set of routing parameters 1430. As used herein, “to route” means to select a route 1495.

[0572] In embodiments, the vehicle routing system 1492 accounts for the routing preference 14100 of the user 1490 when routing the at least one vehicle 1410 within the set of vehicles 1494. In embodiments, the game-based interface Page 46 of 713SFT-108-A-PCT 1499 is disposed for in-vehicle use as indicated in FIG.14 by the line extending from the Game-Based Interface into the box for Vehicle 1. In embodiments, the user 1490 is a rider of the at least one vehicle 1410. In embodiments, the user 1490 is an administrator for a set of roadways to be used by the at least one vehicle 1410 in the set of vehicles 1494. In embodiments, the user 1490 is an administrator for a fleet of vehicles including the set of vehicles 1494. In embodiments, the set of routing parameters 1430 includes at least one of traffic congestion, desired arrival times, preferred routes, fuel efficiency, pollution reduction, accident avoidance, avoiding bad weather, avoiding bad road conditions, reduced fuel consumption, reduced carbon footprint, reduced noise in a region, avoiding high-crime regions, collective satisfaction, maximum speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undivided highways, avoidance of left turns, avoidance of driver-operated vehicles. In embodiments, the game activity 14101 offered in the game-based interface 1499 includes contests. In embodiments, the game activity 14101 offered in the game-based interface 1499 includes entertainment games.

[0573] In embodiments, the game activity 14101 offered in the game-based interface 1499 includes competitive games. In embodiments, the game activity 14101 offered in the game-based interface 1499 includes strategy games. In embodiments, the game activity 14101 offered in the game-based interface 1499 includes scavenger hunts. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a fuel efficiency objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a reduced traffic objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a reduced pollution objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a reduced carbon footprint objective.

[0574] In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a reduced noise in neighborhoods objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a collective satisfaction objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves an avoiding accident scenes objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves an avoiding high-crime areas objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a reduced traffic congestion objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a bad weather avoidance objective.

[0575] In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a maximum travel time objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a maximum speed limit objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves an avoidance of toll road’s objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves an avoidance of city road’s objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves an avoidance of undivided highway’s objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves an avoidance of left turns objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves an avoidance of driver-operated vehicles objective.

[0576] FIG.15 illustrates a method 1500 of game-based coordinated vehicle routing in accordance with embodiments of the systems and methods disclosed herein. At 1502, the method includes presenting, in a game-based interface, a Page 47 of 713SFT-108-A-PCT vehicle route preference-affecting game activity. At 1504, the method includes receiving, through the game-based interface, a user response to the presented game activity. At 1506, the method includes adjusting a routing preference for the user responsive to the received response. At 1508, the method includes determining at least one vehicle-routing parameter used to route vehicles to reflect the adjusted routing preference for routing vehicles. At 1509, the method includes routing, with a vehicle routing system, vehicles in a set of vehicles responsive to the at least one determined vehicle routing parameter adjusted to reflect the adjusted routing preference, wherein routing of the vehicles includes adjusting the determined routing parameter for at least a plurality of vehicles in the set of vehicles.

[0577] Referring to FIG.14 and FIG.15, in embodiments, the method further comprises indicating, by the game-based interface 1499, a reward value 14102 for accepting the game activity 14101. In embodiments, the game-based interface 1499 further comprises a routing preference negotiation system 1436 for a rider to negotiate the reward value 14102 for accepting the game activity 14101. In embodiments, the reward value 14102 is a result of pooling contributions of value from riders in the set of vehicles. In embodiments, at least one routing parameter 1430 used by the vehicle routing system 1492 to route the vehicles 1410 in the set of vehicles 1494 is associated with the game activity 14101 and a user acceptance of the game activity 14101 adjusts (e.g., by the routing adjustment value 1424) the at least one routing parameter 1430 to reflect the routing preference. In embodiments, the user response to the presented game activity 14101 is derived from a user interaction with the game-based interface 1499. In embodiments, the at least one routing parameter used by the vehicle routing system 1492 to route the vehicles 1410 in the set of vehicles 1494 includes at least one of: traffic congestion, desired arrival times, preferred routes, fuel efficiency, pollution reduction, accident avoidance, avoiding bad weather, avoiding bad road conditions, reduced fuel consumption, reduced carbon footprint, reduced noise in a region, avoiding high-crime regions, collective satisfaction, maximum speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undivided highways, avoidance of left turns, and avoidance of driver-operated vehicles.

[0578] In embodiments, the game activity 14101 presented in the game-based interface 1499 includes contests. In embodiments, the game activity 14101 presented in the game-based interface 1499 includes entertainment games. In embodiments, the game activity 14101 presented in the game-based interface 1499 includes competitive games. In embodiments, the game activity 14101 presented in the game-based interface 1499 includes strategy games. In embodiments, the game activity 14101 presented in the game-based interface 1499 includes scavenger hunts. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a fuel efficiency objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a reduced traffic objective.

[0579] In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a reduced pollution objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a reduced carbon footprint objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a reduced noise in neighborhoods objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a collective satisfaction objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves an avoiding accident scene’s objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves an avoiding high-crime areas objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a reduced traffic congestion objective. Page 48 of 713SFT-108-A-PCT

[0580] In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a bad weather avoidance objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a maximum travel time objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a maximum speed limit objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves an avoidance of toll road’s objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves an avoidance of city road’s objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves an avoidance of undivided highway’s objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves an avoidance of left turns objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves an avoidance of driver-operated vehicles objective.

[0581] Referring to FIG. 16, in embodiments, provided herein are transportation systems 1611 having a cognitive system for routing at least one vehicle, wherein the routing is determined at least in part by processing at least one input from a rider interface wherein a rider can obtain a reward 16102 by undertaking an action while in the vehicle. In embodiments, the rider interface may display a set of available rewards for undertaking various actions, such that the rider may select (such as by interacting with a touch screen or audio interface), a set of rewards to pursue, such as by allowing a navigation system of the vehicle (or of a ride-share system of which the user 1690 has at least partial control) or a routing system 1692 of a self-driving vehicle to use the actions that result in rewards to govern routing. For example, selection of a reward for attending a site may result in sending a signal to a navigation or routing system 1692 to set an intermediate destination at the site. As another example, indicating a willingness to watch a piece of content may cause a routing system 1692 to select a route that permits adequate time to view or hear the content.

[0582] An aspect provided herein includes a transportation system 1611, comprising: a cognitive system for routing at least one vehicle 1610, wherein the routing is based, at least in part, by processing at least one input from a rider interface, wherein a reward 16102 is made available to a rider in response to the rider undertaking a predetermined action while in the at least one vehicle 1610.

[0583] An aspect provided herein includes a transportation system 1611 for reward-based coordinated vehicle routing comprising: a reward-based interface 1696 to offer a reward 16102 and through which a user 1690 of users 1691 related to a set of vehicles 1694 indicates a routing preference of the user 1690 related to the reward 16102 by responding to the reward 16102 offered in the reward-based interface 1696; a reward offer response processing circuit 16105 to determine at least one user action resulting from the user response to the reward 16102 and to determine a corresponding effect 16106 on at least one routing parameter 1630; and a vehicle routing system 1692 to use the routing preference 16100 of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles 1694.

[0584] In embodiments, the user 1690 is a rider of at least one vehicle 1610 in the set of vehicles 1694. In embodiments, the user 1690 is an administrator for a set of roadways to be used by at least one vehicle 1610 in the set of vehicles 1694. In embodiments, the user 1690 is an administrator for a fleet of vehicles including the set of vehicles 1694. In embodiments, the reward-based interface 1696 is disposed for in-vehicle use. In embodiments, the at least one routing parameter 1630 includes at least one of: traffic congestion, desired arrival times, preferred routes, fuel efficiency, pollution reduction, accident avoidance, avoiding bad weather, avoiding bad road conditions, reduced fuel consumption, reduced carbon footprint, reduced noise in a region, avoiding high-crime regions, collective satisfaction, Page 49 of 713SFT-108-A-PCT maximum speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undivided highways, avoidance of left turns, and avoidance of driver-operated vehicles. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a fuel efficiency objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a reduced traffic objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve` a reduced pollution objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a reduced carbon footprint objective.

[0585] In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a reduced noise in neighborhoods objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a collective satisfaction objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve` an avoiding accident scenes objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoiding high-crime areas objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a reduced traffic congestion objective.

[0586] In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a bad weather avoidance objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a maximum travel time objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a maximum speed limit objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of toll road’s objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of city road’s objective.

[0587] In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of undivided highway’s objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of left turns objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of driver-operated vehicles objective. Page 50 of 713SFT-108-A-PCT

[0588] FIG.17 illustrates a method 1700 of reward-based coordinated vehicle routing in accordance with embodiments of the systems and methods disclosed herein. At 1702, the method includes receiving through a reward-based interface a response of a user related to a set of vehicles to a reward offered in the reward-based interface. At 1704, the method includes determining a routing preference based on the response of the user. At 1706, the method includes determining at least one user action resulting from the response of the user to the reward. At 1708, the method includes determining a corresponding effect of the at least one user action on at least one routing parameter. At 1709, the method includes governing routing of the set of vehicles responsive to the routing preference and the corresponding effect on the at least one routing parameter.

[0589] In embodiments, the user 1690 is a rider of at least one vehicle 1610 in the set of vehicles 1694. In embodiments, the user 1690 is an administrator for a set of roadways to be used by at least one vehicle 1610 in the set of vehicles 1694. In embodiments, the user 1690 is an administrator for a fleet of vehicles including the set of vehicles 1694.

[0590] In embodiments, the reward-based interface 1696 is disposed for in-vehicle use. In embodiments, the at least one routing parameter 1630 includes at least one of: traffic congestion, desired arrival times, preferred routes, fuel efficiency, pollution reduction, accident avoidance, avoiding bad weather, avoiding bad road conditions, reduced fuel consumption, reduced carbon footprint, reduced noise in a region, avoiding high-crime regions, collective satisfaction, maximum speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undivided highways, avoidance of left turns, and avoidance of driver-operated vehicles. In embodiments, the user 1690 responds to the reward 16102 offered in the reward-based interface 1696 by accepting the reward 16102 offered in the interface, rejecting the reward 16102 offered in the reward-based interface 1696, or ignoring the reward 16102 offered in the reward-based interface 1696. In embodiments, the user 1690 indicates the routing preference by either accepting or rejecting the reward 16102 offered in the reward-based interface 1696. In embodiments, the user 1690 indicates the routing preference by undertaking an action in at least one vehicle 1610 in the set of vehicles 1694 that facilitates transferring the reward 16102 to the user 1690.

[0591] In embodiments, the method further comprises sending, via a reward offer response processing circuit 16105, a signal to the vehicle routing system 1692 to select a vehicle route that permits adequate time for the user 1690 to perform the at least one user action. In embodiments, the method further comprises: sending, via a reward offer response processing circuit 16105, a signal to a vehicle routing system 1692, the signal indicating a destination of a vehicle associated with the at least one user action; and adjusting, by the vehicle routing system 1692, a route of the vehicle 1695 associated with the at least one user action to include the destination. In embodiments, the reward 16102 is associated with achieving a vehicle routing fuel efficiency objective.

[0592] In embodiments, the reward 16102 is associated with achieving a vehicle routing reduced traffic objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing reduced pollution objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing reduced carbon footprint objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing reduced noise in neighborhoods objective. In embodiments, reward 16102 is associated with achieving a vehicle routing collective satisfaction objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing avoiding accident scene’s objective.

[0593] In embodiments, the reward 16102 is associated with achieving a vehicle routing avoiding high-crime areas objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing reduced traffic congestion objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing bad weather avoidance Page 51 of 713SFT-108-A-PCT objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing maximum travel time objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing maximum speed limit objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing avoidance of toll road’s objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing avoidance of city road’s objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing avoidance of undivided highway’s objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing avoidance of left turns objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing avoidance of driver-operated vehicles objective.

[0594] Referring to FIG.18, in embodiments provided herein are transportation systems 1811 having a data processing system 1862 for taking data 18114 from a plurality 1869 of social data sources 18107 and using a neural network 18108 to predict an emerging transportation need 18112 for a group of individuals. Among the various social data sources 18107, such as those described above, a large amount of data is available relating to social groups, such as friend groups, families, workplace colleagues, club members, people having shared interests or affiliations, political groups, and others. The expert system described above can be trained, as described throughout, such as using a training data set of human predictions and / or a model, with feedback of outcomes, to predict the transportation needs of a group. For example, based on a discussion thread of a social group as indicated at least in part on a social network feed, it may become evident that a group meeting or trip will take place, and the system may (such as using location information for respective members, as well as indicators of a set of destinations of the trip), predict where and when each member would need to travel in order to participate. Based on such a prediction, the system could automatically identify and show options for travel, such as available public transportation options, flight options, ride share options, and the like. Such options may include ones by which the group may share transportation, such as indicating a route that results in picking up a set of members of the group for travel together. Social media information may include posts, tweets, comments, chats, photographs, and the like and may be processed as noted above.

[0595] An aspect provided herein includes a system 1811 for transportation, comprising: a data processing system 1862 for taking data 18114 from a plurality 1869 of social data sources 18107 and using a neural network 18108 to predict an emerging transportation need 18112 for a group of individuals 18110.

[0596] FIG. 19 illustrates a method 1900 of predicting a common transportation need for a group in accordance with embodiments of the systems and methods disclosed herein. At 1902, the method includes gathering social media- sourced data about a plurality of individuals, the data being sourced from a plurality of social media sources. At 1904, the method includes processing the data to identify a subset of the plurality of individuals who form a social group based on group affiliation references in the data. At 1906, the method includes detecting keywords in the data indicative of a transportation need. At 1908, the method includes using a neural network trained to predict transportation needs based on the detected keywords to identify the common transportation need for the subset of the plurality of individuals.

[0597] Referring to FIG.18 and FIG.19, in embodiments, the neural network 18108 is a convolutional neural network 18113. In embodiments, the neural network 18108 is trained based on a model that facilitates matching phrases in social media with transportation activity. In embodiments, the neural network 18108 predicts at least one of a destination and an arrival time for the subset 18110 of the plurality of individuals sharing the common transportation need. In embodiments, the neural network 18108 predicts the common transportation need based on analysis of transportation need-indicative keywords detected in a discussion thread among a portion of individuals in the social Page 52 of 713SFT-108-A-PCT group. In embodiments, the method further comprises identifying at least one shared transportation service 18111 that facilitates a portion of the social group meeting the predicted common transportation need 18112. In embodiments, the at least one shared transportation service comprises generating a vehicle route that facilitates picking up the portion of the social group.

[0598] FIG. 20 illustrates a method 2000 of predicting a group transportation need for a group in accordance with embodiments of the systems and methods disclosed herein. At 2002, the method includes gathering social media- sourced data about a plurality of individuals, the data being sourced from a plurality of social media sources. At 2004, the method includes processing the data to identify a subset of the plurality of individuals who share the group transportation need. At 2006, the method includes detecting keywords in the data indicative of the group transportation need for the subset of the plurality of individuals. At 2008, the method includes predicting the group transportation need using a neural network trained to predict transportation needs based on the detected keywords. At 2009, the method includes directing a vehicle routing system to meet the group transportation need.

[0599] Referring to FIG.18 and FIG.20, in embodiments, the neural network 18108 is a convolutional neural network 18113. In embodiments, directing the vehicle routing system to meet the group transportation need involves routing a plurality of vehicles to a destination derived from the social media-sourced data 18114. In embodiments, the neural network 18108 is trained based on a model that facilitates matching phrases in the social media-sourced data 18114 with transportation activities. In embodiments, the method further comprises predicting, by the neural network 18108, at least one of a destination and an arrival time for the subset 18110 of the plurality 18109 of individuals sharing the group transportation need. In embodiments, the method further comprises predicting, by the neural network 18108, the group transportation need based on an analysis of transportation need-indicative keywords detected in a discussion thread in the social media-sourced data 18114. In embodiments, the method further comprises identifying at least one shared transportation service 18111 that facilitates meeting the predicted group transportation need for at least a portion of the subset 18110 of the plurality of individuals. In embodiments, the at least one shared transportation service 18111 comprises generating a vehicle route that facilitates picking up the at least the portion of the subset 18110 of the plurality of individuals.

[0600] FIG.21 illustrates a method 2100 of predicting a group transportation need in accordance with embodiments of the systems and methods disclosed herein. At 2102, the method includes gathering social media-sourced data from a plurality of social media sources. At 2104, the method includes processing the data to identify an event. At 2106, the method includes detecting keywords in the data indicative of the event to determine a transportation need associated with the event. At 2108, the method includes using a neural network trained to predict transportation needs based at least in part on social media-sourced data to direct a vehicle routing system to meet the transportation need.

[0601] Referring to FIG.18 and FIG.21, in embodiments, the neural network 18108 is a convolutional neural network 18113. In embodiments, the vehicle routing system is directed to meet the transportation need by routing a plurality of vehicles to a location associated with the event. In embodiments, the vehicle routing system is directed to meet the transportation need by routing a plurality of vehicles to avoid a region proximal to a location associated with the event. In embodiments, the vehicle routing system is directed to meet the transportation need by routing vehicles associated with users whose social media-sourced data 18114 do not indicate the transportation need to avoid a region proximal to a location associated with the event. In embodiments, the method further comprises presenting at least one transportation service for satisfying the transportation need. In embodiments, the neural network 18108 is trained based on a model that facilitates matching phrases in social media-sourced data 18114 with transportation activity. Page 53 of 713SFT-108-A-PCT

[0602] In embodiments, the neural network 18108 predicts at least one of a destination and an arrival time for individuals attending the event. In embodiments, the neural network 18108 predicts the transportation need based on analysis of transportation need-indicative keywords detected in a discussion thread in the social media-sourced data 18114. In embodiments, the method further comprises identifying at least one shared transportation service that facilitates meeting the predicted transportation need for at least a subset of individuals identified in the social media- sourced data 18114. In embodiments, the at least one shared transportation service comprises generating a vehicle route that facilitates picking up the portion of the subset of individuals identified in the social media-sourced data 18114.

[0603] Referring to FIG.22, in embodiments provided herein are transportation systems 2211 having a data processing system 2262 for taking social media data 22114 from a plurality 2269 of social data sources 22107 and using a hybrid neural network 2247 to optimize an operating state of a transportation system 22111 based on processing the social data sources 22107 with the hybrid neural network 2247. A hybrid neural network 2247 may have, for example, a neural network component that makes a classification or prediction based on processing social media data 22114 (such as predicting a high level of attendance of an event by processing images on many social media feeds that indicate interest in the event by many people, prediction of traffic, classification of interest by an individual in a topic, and many others) and another component that optimizes an operating state of a transportation system, such as an in-vehicle state, a routing state (for an individual vehicle 2210 or a set of vehicles 2294), a user-experience state, or other state described throughout this disclosure (e.g., routing an individual early to a venue like a music festival where there is likely to be very high attendance, playing music content in a vehicle 2210 for bands who will be at the music festival, or the like).

[0604] An aspect provided herein includes a system for transportation, comprising: a data processing system 2262 for taking social media data 22114 from a plurality 2269 of social data sources 22107 and using a hybrid neural network 2247 to optimize an operating state of a transportation system based on processing the data 22114 from the plurality 2269 of social data sources 22107 with the hybrid neural network 2247.

[0605] An aspect provided herein includes a hybrid neural network system 22115 for transportation system optimization, the hybrid neural network system 22115 comprising a hybrid neural network 2247, including: a first neural network 2222 that predicts a localized effect 22116 on a transportation system through analysis of social medial data 22114 sourced from a plurality 2269 of social media data sources 22107; and a second neural network 2220 that optimizes an operating state of the transportation system based on the predicted localized effect 22116.

[0606] In embodiments, at least one of the first neural network 2222 and the second neural network 2220 is a convolutional neural network. In embodiments, the second neural network 2220 is to optimize an in-vehicle rider experience state. In embodiments, the first neural network 2222 identifies a set of vehicles 2294 contributing to the localized effect 22116 based on correlation of vehicle location and an area of the localized effect 22116. In embodiments, the second neural network 2220 is to optimize a routing state of the transportation system for vehicles proximal to a location of the localized effect 22116. In embodiments, the hybrid neural network 2247 is trained for at least one of the predicting and optimizing based on keywords in the social media data indicative of an outcome of a transportation system optimization action. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on social media posts.

[0607] In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on social media feeds. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and Page 54 of 713SFT-108-A-PCT optimizing based on ratings derived from the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on like or dislike activity detected in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on indications of relationships in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on user behavior detected in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on discussion threads in the social media data 22114.

[0608] In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on chats in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on photographs in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on traffic-affecting information in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on an indication of a specific individual at a location in the social media data 22114. In embodiments, the specific individual is a celebrity. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based a presence of a rare or transient phenomena at a location in the social media data 22114.

[0609] In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based a commerce-related event at a location in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based an entertainment event at a location in the social media data 22114. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes traffic conditions. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes weather conditions. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes entertainment options.

[0610] In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes risk-related conditions. In embodiments, the risk-related conditions include crowds gathering for potentially dangerous reasons. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes commerce-related conditions. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes goal-related conditions.

[0611] In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes estimates of attendance at an event. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes predictions of attendance at an event. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes modes of transportation. In embodiments, the modes of transportation include car traffic. In embodiments, the modes of transportation include public transportation options.

[0612] In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes hash tags. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes trending of topics. In embodiments, an outcome of a transportation system optimization action is reducing fuel consumption. In embodiments, an outcome of a transportation system optimization action is reducing traffic congestion. In embodiments, an outcome of a transportation system optimization action is reduced pollution. In embodiments, an outcome of a transportation system optimization action is bad weather avoidance. In embodiments, Page 55 of 713SFT-108-A-PCT an operating state of the transportation system being optimized includes an in-vehicle state. In embodiments, an operating state of the transportation system being optimized includes a routing state.

[0613] In embodiments, the routing state is for an individual vehicle 2210. In embodiments, the routing state is for a set of vehicles 2294. In embodiments, an operating state of the transportation system being optimized includes a user- experience state.

[0614] FIG.23 illustrates a method 2300 of optimizing an operating state of a transportation system in accordance with embodiments of the systems and methods disclosed herein. At 2302 the method includes gathering social media- sourced data about a plurality of individuals, the data being sourced from a plurality of social media sources. At 2304 the method includes optimizing, using a hybrid neural network, the operating state of the transportation system. At 2306 the method includes predicting, by a first neural network of the hybrid neural network, an effect on the transportation system through an analysis of the social media-sourced data. At 2308 the method includes optimizing, by a second neural network of the hybrid neural network, at least one operating state of the transportation system responsive to the predicted effect thereon.

[0615] Referring to FIG.22 and FIG.23, in embodiments, at least one of the first neural network 2222 and the second neural network 2220 is a convolutional neural network. In embodiments, the second neural network 2220 optimizes an in-vehicle rider experience state. In embodiments, the first neural network 2222 identifies a set of vehicles contributing to the effect based on correlation of vehicle location and an effect area. In embodiments, the second neural network 2220 optimizes a routing state of the transportation system for vehicles proximal to a location of the effect.

[0616] In embodiments, the hybrid neural network 2247 is trained for at least one of the predicting and optimizing based on keywords in the social media data indicative of an outcome of a transportation system optimization action. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on social media posts. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on social media feeds. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on ratings derived from the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on like or dislike activity detected in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on indications of relationships in the social media data 22114.

[0617] In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on user behavior detected in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on discussion threads in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on chats in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on photographs in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on traffic-affecting information in the social media data 22114.

[0618] In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on an indication of a specific individual at a location in the social media data. In embodiments, the specific individual is a celebrity. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based a presence of a rare or transient phenomena at a location in the social media data. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based a commerce-related event at a Page 56 of 713SFT-108-A-PCT location in the social media data. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based an entertainment event at a location in the social media data. In embodiments, the social media data analyzed to predict an effect on a transportation system includes traffic conditions.

[0619] In embodiments, the social media data analyzed to predict an effect on a transportation system includes weather conditions. In embodiments, the social media data analyzed to predict an effect on a transportation system includes entertainment options. In embodiments, the social media data analyzed to predict an effect on a transportation system includes risk-related conditions. In embodiments, the risk-related conditions include crowds gathering for potentially dangerous reasons. In embodiments, the social media data analyzed to predict an effect on a transportation system includes commerce-related conditions. In embodiments, the social media data analyzed to predict an effect on a transportation system includes goal-related conditions.

[0620] In embodiments, the social media data analyzed to predict an effect on a transportation system includes estimates of attendance at an event. In embodiments, the social media data analyzed to predict an effect on a transportation system includes predictions of attendance at an event. In embodiments, the social media data analyzed to predict an effect on a transportation system includes modes of transportation. In embodiments, the modes of transportation include car traffic. In embodiments, the modes of transportation include public transportation options. In embodiments, the social media data analyzed to predict an effect on a transportation system includes hash tags. In embodiments, the social media data analyzed to predict an effect on a transportation system includes trending of topics.

[0621] In embodiments, an outcome of a transportation system optimization action is reducing fuel consumption. In embodiments, an outcome of a transportation system optimization action is reducing traffic congestion. In embodiments, an outcome of a transportation system optimization action is reduced pollution. In embodiments, an outcome of a transportation system optimization action is bad weather avoidance. In embodiments, the operating state of the transportation system being optimized includes an in-vehicle state. In embodiments, the operating state of the transportation system being optimized includes a routing state. In embodiments, the routing state is for an individual vehicle. In embodiments, the routing state is for a set of vehicles. In embodiments, the operating state of the transportation system being optimized includes a user-experience state.

[0622] FIG.24 illustrates a method 2400 of optimizing an operating state of a transportation system in accordance with embodiments of the systems and methods disclosed herein. At 2402 the method includes using a first neural network of a hybrid neural network to classify social media data sourced from a plurality of social media sources as affecting a transportation system. At 2404 the method includes using a second network of the hybrid neural network to predict at least one operating objective of the transportation system based on the classified social media data. At 2406 the method includes using a third network of the hybrid neural network to optimize the operating state of the transportation system to achieve the at least one operating objective of the transportation system.

[0623] Referring to FIG. 22 and FIG. 24, in embodiments, at least one of the neural networks in the hybrid neural network 2247 is a convolutional neural network.

[0624] Referring to FIG.25, in embodiments provided herein are transportation systems 2511 having a data processing system 2562 for taking social media data 25114 from a plurality of social data sources 25107 and using a hybrid neural network 2547 to optimize an operating state 2545 of a vehicle 2510 based on processing the social data sources with the hybrid neural network 2547. In embodiments, the hybrid neural network 2547 can include one neural network category for prediction, another for classification, and another for optimization of one or more operating states, such as based on optimizing one or more desired outcomes (such a providing efficient travel, highly satisfying rider Page 57 of 713SFT-108-A-PCT experiences, comfortable rides, on-time arrival, or the like). Social data sources 2569 may be used by distinct neural network categories (such as any of the types described herein) to predict travel times, to classify content such as for profiling interests of a user, to predict objectives for a transportation plan (such as what will provide overall satisfaction for an individual or a group) and the like. Social data sources 2569 may also inform optimization, such as by providing indications of successful outcomes (e.g., a social data source 25107 like a Facebook feed might indicate that a trip was “amazing” or “horrible,” a Yelp review might indicate a restaurant was terrible, or the like). Thus, social data sources 2569, by contributing to outcome tracking, can be used to train a system to optimize transportation plans, such as relating to timing, destinations, trip purposes, what individuals should be invited, what entertainment options should be selected, and many others.

[0625] An aspect provided herein includes a transportation system 2511, comprising: a data processing system 2562 for taking social media data 25114 from a plurality of social data sources 25107 and using a hybrid neural network 2547 to optimize an operating state 2545 of a vehicle 2510 based on processing the data 25114 from the plurality of social data sources 25107 with the hybrid neural network 2547.

[0626] FIG.26 illustrates a method 2600 of optimizing an operating state of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At 2602 the method includes classifying, using a first neural network 2522 (FIG.25) of a hybrid neural network, social media data 25119 (FIG.25) sourced from a plurality of social media sources as affecting a transportation system. At 2604 the method includes predicting, using a second neural network 2520 (FIG.25) of the hybrid neural network, one or more effects 25118 (FIG.25) of the classified social media data on the transportation system. At 2606 the method includes optimizing, using a third neural network 25117 (FIG.25) of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

[0627] Referring to FIG. 25 and FIG. 26, in embodiments, at least one of the neural networks in the hybrid neural network 2547 is a convolutional neural network. In embodiments, the social media data 25114 includes social media posts. In embodiments, the social media data 25114 includes social media feeds. In embodiments, the social media data 25114 includes like or dislike activity detected in the social media. In embodiments, the social media data 25114 includes indications of relationships. In embodiments, the social media data 25114 includes user behavior. In embodiments, the social media data 25114 includes discussion threads. In embodiments, the social media data 25114 includes chats. In embodiments, the social media data 25114 includes photographs.

[0628] In embodiments, the social media data 25114 includes traffic-affecting information. In embodiments, the social media data 25114 includes an indication of a specific individual at a location. In embodiments, the social media data 25114 includes an indication of a celebrity at a location. In embodiments, the social media data 25114 includes presence of a rare or transient phenomena at a location. In embodiments, the social media data 25114 includes a commerce-related event. In embodiments, the social media data 25114 includes an entertainment event at a location. In embodiments, the social media data 25114 includes traffic conditions. In embodiments, the social media data 25114 includes weather conditions. In embodiments, the social media data 25114 includes entertainment options.

[0629] In embodiments, the social media data 25114 includes risk-related conditions. In embodiments, the social media data 25114 includes predictions of attendance at an event. In embodiments, the social media data 25114 includes estimates of attendance at an event. In embodiments, the social media data 25114 includes modes of transportation used with an event. In embodiments, the effect 25118 on the transportation system includes reducing fuel consumption. In embodiments, the effect 25118 on the transportation system includes reducing traffic congestion. In embodiments, Page 58 of 713SFT-108-A-PCT the effect 25118 on the transportation system includes reduced carbon footprint. In embodiments, the effect 25118 on the transportation system includes reduced pollution.

[0630] In embodiments, the optimized state 2544 of the at least one vehicle 2510 is an operating state 2545 of the vehicle. In embodiments, the optimized state of the at least one vehicle includes an in-vehicle state. In embodiments, the optimized state of the at least one vehicle includes a rider state. In embodiments, the optimized state of the at least one vehicle includes a routing state. In embodiments, the optimized state of the at least one vehicle includes user experience state. In embodiments, a characterization of an outcome of the optimizing in the social media data 25114 is used as feedback to improve the optimizing. In embodiments, the feedback includes likes and dislikes of the outcome. In embodiments, the feedback includes social medial activity referencing the outcome.

[0631] In embodiments, the feedback includes trending of social media activity referencing the outcome. In embodiments, the feedback includes hash tags associated with the outcome. In embodiments, the feedback includes ratings of the outcome. In embodiments, the feedback includes requests for the outcome.

[0632] FIG. 26A illustrates a method 26A00 of optimizing an operating state of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At 26A02 the method includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system. At 26A04 the method includes predicting, using a second neural network of the hybrid neural network, at least one vehicle-operating objective of the transportation system based on the classified social media data. At 26A06 the method includes optimizing, using a third neural network of the hybrid neural network, a state of a vehicle in the transportation system to achieve the at least one vehicle-operating objective of the transportation system.

[0633] Referring to FIG. 25 and FIG. 26A, in embodiments, at least one of the neural networks in the hybrid neural network 2547 is a convolutional neural network. In embodiments, the vehicle-operating objective comprises achieving a rider state of at least one rider in the vehicle. In embodiments, the social media data 25114 includes social media posts.

[0634] In embodiments, the social media data 25114 includes social media feeds. In embodiments, the social media data 25114 includes like and dislike activity detected in the social media. In embodiments, the social media data 25114 includes indications of relationships. In embodiments, the social media data 25114 includes user behavior. In embodiments, the social media data 25114 includes discussion threads. In embodiments, the social media data 25114 includes chats. In embodiments, the social media data 25114 includes photographs. In embodiments, the social media data 25114 includes traffic-affecting information.

[0635] In embodiments, the social media data 25114 includes an indication of a specific individual at a location. In embodiments, the social media data 25114 includes an indication of a celebrity at a location. In embodiments, the social media data 25114 includes presence of a rare or transient phenomena at a location. In embodiments, the social media data 25114 includes a commerce-related event. In embodiments, the social media data 25114 includes an entertainment event at a location. In embodiments, the social media data 25114 includes traffic conditions. In embodiments, the social media data 25114 includes weather conditions. In embodiments, the social media data 25114 includes entertainment options.

[0636] In embodiments, the social media data 25114 includes risk-related conditions. In embodiments, the social media data 25114 includes predictions of attendance at an event. In embodiments, the social media data 25114 includes estimates of attendance at an event. In embodiments, the social media data 25114 includes modes of transportation Page 59 of 713SFT-108-A-PCT used with an event. In embodiments, the effect on the transportation system includes reducing fuel consumption. In embodiments, the effect on the transportation system includes reducing traffic congestion. In embodiments, the effect on the transportation system includes reduced carbon footprint. In embodiments, the effect on the transportation system includes reduced pollution. In embodiments, the optimized state of the vehicle is an operating state of the vehicle.

[0637] In embodiments, the optimized state of the vehicle includes an in-vehicle state. In embodiments, the optimized state of the vehicle includes a rider state. In embodiments, the optimized state of the vehicle includes a routing state. In embodiments, the optimized state of the vehicle includes user experience state. In embodiments, a characterization of an outcome of the optimizing in the social media data is used as feedback to improve the optimizing. In embodiments, the feedback includes likes or dislikes of the outcome. In embodiments, the feedback includes social medial activity referencing the outcome. In embodiments, the feedback includes trending of social media activity referencing the outcome.

[0638] In embodiments, the feedback includes hash tags associated with the outcome. In embodiments, the feedback includes ratings of the outcome. In embodiments, the feedback includes requests for the outcome.

[0639] Referring to FIG.27, in embodiments provided herein are transportation systems 2711 having a data processing system 2762 for taking social data 27114 from a plurality 2769 of social data sources 27107 and using a hybrid neural network 2747 to optimize satisfaction 27121 of at least one rider 27120 in a vehicle 2710 based on processing the social data sources with the hybrid neural network 2747. Plurality 2769 of social data sources may be used, for example, to predict what entertainment options are most likely to be effective for a rider 27120 by one neural network category, while another neural network category may be used to optimize a routing plan (such as based on social data that indicates likely traffic, points of interest, or the like). Social data 27114 may also be used for outcome tracking and feedback to optimize the system, both as to entertainment options and as to transportation planning, routing, or the like.

[0640] An aspect provided herein includes a transportation system 2711, comprising: a data processing system 2762 for taking social data 27114 from a plurality 2769 of social data sources 27107 and using a hybrid neural network 2747 to optimize satisfaction 27121 of at least one rider 27120 in a vehicle 2710 based on processing the social data 27114 from the plurality 2769 of social data sources 27107 with the hybrid neural network 2747.

[0641] FIG.28 illustrates a method 2800 of optimizing rider satisfaction in accordance with embodiments of the systems and methods disclosed herein. At 2802 the method includes classifying, using a first neural network 2722 (FIG.27) of a hybrid neural network, social media data 27119 (FIG. 27) sourced from a plurality of social media sources as indicative of an effect on a transportation system. At 2804 the method includes predicting, using a second neural network 2720 (FIG.27) of the hybrid neural network, at least one aspect 27122 (FIG.27) of rider satisfaction affected by an effect on the transportation system derived from the social media data classified as indicative of an effect on the transportation system. At 2806 the method includes optimizing, using a third neural network 27117 (FIG.27) of the hybrid neural network, the at least one aspect of rider satisfaction for at least one rider occupying a vehicle in the transportation system.

[0642] Referring to FIG. 27 and FIG. 28, in embodiments, at least one of the neural networks in the hybrid neural network 2547 is a convolutional neural network. In embodiments, the at least one aspect of rider satisfaction 27121 is optimized by predicting an entertainment option for presenting to the rider. In embodiments, the at least one aspect of rider satisfaction 27121 is optimized by optimizing route planning for a vehicle occupied by the rider. In embodiments, the at least one aspect of rider satisfaction 27121 is a rider state and optimizing the aspects of rider satisfaction Page 60 of 713SFT-108-A-PCT comprising optimizing the rider state. In embodiments, social media data specific to the rider is analyzed to determine at least one optimizing action likely to optimize the at least one aspect of rider satisfaction 27121. In embodiments, the optimizing action is selected from the group of actions consisting of adjusting a routing plan to include passing points of interest to the user, avoiding traffic congestion predicted from the social media data, and presenting entertainment options.

[0643] In embodiments, the social media data includes social media posts. In embodiments, the social media data includes social media feeds. In embodiments, the social media data includes like or dislike activity detected in the social media. In embodiments, the social media data includes indications of relationships. In embodiments, the social media data includes user behavior. In embodiments, the social media data includes discussion threads. In embodiments, the social media data includes chats. In embodiments, the social media data includes photographs.

[0644] In embodiments, the social media data includes traffic-affecting information. In embodiments, the social media data includes an indication of a specific individual at a location. In embodiments, the social media data includes an indication of a celebrity at a location. In embodiments, the social media data includes presence of a rare or transient phenomena at a location. In embodiments, the social media data includes a commerce-related event. In embodiments, the social media data includes an entertainment event at a location. In embodiments, the social media data includes traffic conditions. In embodiments, the social media data includes weather conditions. In embodiments, the social media data includes entertainment options. In embodiments, the social media data includes risk-related conditions. In embodiments, the social media data includes predictions of attendance at an event. In embodiments, the social media data includes estimates of attendance at an event. In embodiments, the social media data includes modes of transportation used with an event. In embodiments, the effect on the transportation system includes reducing fuel consumption. In embodiments, the effect on the transportation system includes reducing traffic congestion. In embodiments, the effect on the transportation system includes reduced carbon footprint. In embodiments, the effect on the transportation system includes reduced pollution. In embodiments, the optimized at least one aspect of rider satisfaction is an operating state of the vehicle. In embodiments, the optimized at least one aspect of rider satisfaction includes an in-vehicle state. In embodiments, the optimized at least one aspect of rider satisfaction includes a rider state. In embodiments, the optimized at least one aspect of rider satisfaction includes a routing state. In embodiments, the optimized at least one aspect of rider satisfaction includes user experience state.

[0645] In embodiments, a characterization of an outcome of the optimizing in the social media data is used as feedback to improve the optimizing. In embodiments, the feedback includes likes or dislikes of the outcome. In embodiments, the feedback includes social medial activity referencing the outcome. In embodiments, the feedback includes trending of social media activity referencing the outcome. In embodiments, the feedback includes hash tags associated with the outcome. In embodiments, the feedback includes ratings of the outcome. In embodiments, the feedback includes requests for the outcome.

[0646] An aspect provided herein includes a rider satisfaction system 27123 for optimizing rider satisfaction 27121, the system comprising: a first neural network 2722 of a hybrid neural network 2747 to classify social media data 27114 sourced from a plurality 2769 of social media sources 27107 as indicative of an effect on a transportation system 2711; a second neural network 2720 of the hybrid neural network 2747 to predict at least one aspect 27122 of rider satisfaction 27121 affected by an effect on the transportation system derived from the social media data classified as indicative of the effect on the transportation system; and a third neural network 27117 of the hybrid neural network 2747 to optimize the at least one aspect of rider satisfaction 27121 for at least one rider 2744 occupying a vehicle 2710 Page 61 of 713SFT-108-A-PCT in the transportation system 2711. In embodiments, at least one of the neural networks in the hybrid neural network 2747 is a convolutional neural network.

[0647] In embodiments, the at least one aspect of rider satisfaction 27121 is optimized by predicting an entertainment option for presenting to the rider 2744. In embodiments, the at least one aspect of rider satisfaction 27121 is optimized by optimizing route planning for a vehicle 2710 occupied by the rider 2744. In embodiments, the at least one aspect of rider satisfaction 27121 is a rider state 2737 and optimizing the at least one aspect of rider satisfaction 27121 comprises optimizing the rider state 2737. In embodiments, social media data specific to the rider 2744 is analyzed to determine at least one optimizing action likely to optimize the at least one aspect of rider satisfaction 27121. In embodiments, the at least one optimizing action is selected from the group consisting of: adjusting a routing plan to include passing points of interest to the user, avoiding traffic congestion predicted from the social media data, deriving an economic benefit, deriving an altruistic benefit, and presenting entertainment options.

[0648] In embodiments, the economic benefit is saved fuel. In embodiments, the altruistic benefit is reduction of environmental impact. In embodiments, the social media data includes social media posts. In embodiments, the social media data includes social media feeds. In embodiments, the social media data includes like or dislike activity detected in the social media. In embodiments, the social media data includes indications of relationships. In embodiments, the social media data includes user behavior. In embodiments, the social media data includes discussion threads. In embodiments, the social media data includes chats. In embodiments, the social media data includes photographs. In embodiments, the social media data includes traffic-affecting information. In embodiments, the social media data includes an indication of a specific individual at a location.

[0649] In embodiments, the social media data includes an indication of a celebrity at a location. In embodiments, the social media data includes presence of a rare or transient phenomena at a location. In embodiments, the social media data includes a commerce-related event. In embodiments, the social media data includes an entertainment event at a location. In embodiments, the social media data includes traffic conditions. In embodiments, the social media data includes weather conditions. In embodiments, the social media data includes entertainment options. In embodiments, the social media data includes risk-related conditions. In embodiments, the social media data includes predictions of attendance at an event. In embodiments, the social media data includes estimates of attendance at an event. In embodiments, the social media data includes modes of transportation used with an event.

[0650] In embodiments, the effect on the transportation system includes reducing fuel consumption. In embodiments, the effect on the transportation system includes reducing traffic congestion. In embodiments, the effect on the transportation system includes reduced carbon footprint. In embodiments, the effect on the transportation system includes reduced pollution. In embodiments, the optimized at least one aspect of rider satisfaction is an operating state of the vehicle. In embodiments, the optimized at least one aspect of rider satisfaction includes an in-vehicle state. In embodiments, the optimized at least one aspect of rider satisfaction includes a rider state. In embodiments, the optimized at least one aspect of rider satisfaction includes a routing state. In embodiments, the optimized at least one aspect of rider satisfaction includes user experience state. In embodiments, a characterization of an outcome of the optimizing in the social media data is used as feedback to improve the optimizing. In embodiments, the feedback includes likes or dislikes of the outcome. In embodiments, the feedback includes social medial activity referencing the outcome. In embodiments, the feedback includes trending of social media activity referencing the outcome. In embodiments, the feedback includes hash tags associated with the outcome. In embodiments, the feedback includes ratings of the outcome. In embodiments, the feedback includes requests for the outcome. Page 62 of 713SFT-108-A-PCT

[0651] Referring to FIG.29, in embodiments provided herein are transportation systems 2911 having a hybrid neural network 2947 wherein one neural network 2922 processes a sensor input 29125 about a rider 2944 of a vehicle 2910 to determine an emotional state 29126 and another neural network optimizes at least one operating parameter 29124 of the vehicle to improve the rider’s emotional state 2966. For example, a neural net 2922 that includes one or more perceptrons 29127 that mimic human senses may be used to mimic or assist with determining the likely emotional state of a rider 29126 based on the extent to which various senses have been stimulated, while another neural network 2920 is used in an expert system that performs random and / or systematized variations of various combinations of operating parameters (such as entertainment settings, seat settings, suspension settings, route types and the like) with genetic programming that promotes favorable combinations and eliminates unfavorable ones, optionally based on input from the output of the perceptron-containing neural network 2922 that predict emotional state. These and many other such combinations are encompassed by the present disclosure. In Fig 29, perceptrons 29127 are depicted as optional.

[0652] An aspect provided herein includes a transportation system 2911, comprising: a hybrid neural network 2947 wherein one neural network 2922 processes a sensor input 29125 corresponding to a rider 2944 of a vehicle 2910 to determine an emotional state 2966 of the rider 2944 and another neural network 2920 optimizes at least one operating parameter 29124 of the vehicle to improve the emotional state 2966 of the rider 2944.

[0653] An aspect provided herein includes a hybrid neural network 2947 for rider satisfaction, comprising: a first neural network 2922 to detect a detected emotional state 29126 of a rider 2944 occupying a vehicle 2910 through analysis of the sensor input 29125 gathered from sensors 2925 deployed in a vehicle 2910 for gathering physiological conditions of the rider; and a second neural network 2920 to optimize, for achieving a favorable emotional state of the rider, an operational parameter 29124 of the vehicle in response to the detected emotional state 29126 of the rider.

[0654] In embodiments, the first neural network 2922 is a recurrent neural network and the second neural network 2920 is a radial basis function neural network. In embodiments, at least one of the neural networks in the hybrid neural network 2947 is a convolutional neural network. In embodiments, the second neural network 2920 is to optimize the operational parameter 29124 based on a correlation between a vehicle operating state 2945 and a rider emotional state 2966 of the rider. In embodiments, the second neural network 2920 optimizes the operational parameter 29124 in real time responsive to the detecting of the detected emotional state 29126 of the rider 2944 by the first neural network 2922. In embodiments, the first neural network 2922 comprises a plurality of connected nodes that form a directed cycle, the first neural network 2922 further facilitating bi-directional flow of data among the connected nodes. In embodiments, the operational parameter 29124 that is optimized affects at least one of: a route of the vehicle, in- vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, and a proximity to other vehicles along the route.

[0655] An aspect provided herein includes an artificial intelligence system 2936 for optimizing rider satisfaction, comprising: a hybrid neural network 2947, including: a recurrent neural network (e.g., in FIG.29, neural network 2922 may be a recurrent neural network) to indicate a change in an emotional state of a rider 2944 in a vehicle 2910 through recognition of patterns of physiological data of the rider captured by at least one sensor 2925 deployed for capturing rider emotional state-indicative data while occupying the vehicle 2910; and a radial basis function neural network (e.g., in FIG. 29, the second neural network 2920 may be a radial basis function neural network) to optimize, for achieving a favorable emotional state of the rider, an operational parameter 29124 of the vehicle in response to the Page 63 of 713SFT-108-A-PCT indication of change in the emotional state of the rider. In embodiments, the operational parameter 29124 of the vehicle that is to be optimized is to be determined and adjusted to induce the favorable emotional state of the rider.

[0656] An aspect provided herein includes an artificial intelligence system 2936 for optimizing rider satisfaction, comprising: a hybrid neural network 2947, including: a convolutional neural network (in FIG.29, neural network 1, depicted at reference numeral 2922, may optionally be a convolutional neural network) to indicate a change in an emotional state of a rider in a vehicle through recognitions of patterns of visual data of the rider captured by at least one image sensor (in FIG.29, the sensor 2925 may optionally be an image sensor) deployed for capturing images of the rider while occupying the vehicle; and a second neural network 2920 to optimize, for achieving a favorable emotional state of the rider, an operational parameter 29124 of the vehicle in response to the indication of change in the emotional state of the rider.

[0657] In embodiments, the operational parameter 19124 of the vehicle that is to be optimized is to be determined and adjusted to induce the favorable emotional state of the rider.

[0658] Referring to FIG. 30, in embodiments provided herein are transportation systems 3011 having an artificial intelligence system 3036 for processing feature vectors of an image of a face of a rider in a vehicle to determine an emotional state and optimizing at least one operating parameter of the vehicle to improve the rider’s emotional state. A face may be classified based on images from in-vehicle cameras, available cellphone or other mobile device cameras, or other sources. An expert system, optionally trained based on a training set of data provided by humans or trained by deep learning, may learn to adjust vehicle parameters (such as any described herein) to provide improved emotional states. For example, if a rider’s face indicates stress, the vehicle may select a less stressful route, play relaxing music, play humorous content, or the like.

[0659] An aspect provided herein includes a transportation system 3011, comprising: an artificial intelligence system 3036 (with a hybrid neural network 3047) for processing feature vectors 30130 of an image 30129 of a face 30128 of a rider 3044 in a vehicle 3010 to determine an emotional state 3066 of the rider and optimizing an operational parameter 30124 of the vehicle to improve the emotional state 3066 of the rider 3044.

[0660] In embodiments, the artificial intelligence system 3036 includes: a first neural network 3022 to detect the emotional state 30126 of the rider through recognition of patterns of the feature vectors 30130 of the image 30129 of the face 30128 of the rider 3044 in the vehicle 3010, the feature vectors 30130 indicating at least one of a favorable emotional state of the rider and an unfavorable emotional state of the rider; and a second neural network 3020 to optimize, for achieving the favorable emotional state of the rider, the operational parameter 30124 of the vehicle in response to the detected emotional state 30126 of the rider.

[0661] In embodiments, the first neural network 3022 is a recurrent neural network and the second neural network 3020 is a radial basis function neural network. In embodiments, the second neural network 3020 optimizes the operational parameter 30124 based on a correlation between the vehicle operating state 3045 and the emotional state 3066 of the rider. In embodiments, the second neural network 3020 is to determine an optimum value for the operational parameter of the vehicle, and the transportation system 3011 is to adjust the operational parameter 30124 of the vehicle to the optimum value to induce the favorable emotional state of the rider. In embodiments, the first neural network 3022 further learns to classify the patterns in the feature vectors and associate the patterns with a set of emotional states and changes thereto by processing a training data set 30131. In embodiments, the training data set 30131 is sourced from at least one of a stream of data from an unstructured data source, a social media source, a wearable device, an in- vehicle sensor, a rider helmet, a rider headgear, and a rider voice recognition system. Page 64 of 713SFT-108-A-PCT

[0662] In embodiments, the second neural network 3020 optimizes the operational parameter 30124 in real time responsive to the detecting of the emotional state of the rider by the first neural network 3022. In embodiments, the first neural network 3022 is to detect a pattern of the feature vectors. In embodiments, the pattern is associated with a change in the emotional state of the rider from a first emotional state to a second emotional state. In embodiments, the second neural network 3020 optimizes the operational parameter of the vehicle in response to the detection of the pattern associated with the change in the emotional state. In embodiments, the first neural network 3022 comprises a plurality of interconnected nodes that form a directed cycle, the first neural network 3022 further facilitating bi- directional flow of data among the interconnected nodes. In embodiments, the transportation system 3011 further comprises: a feature vector generation system to process a set of images of the face of the rider, the set of images captured over an interval of time from by a plurality of image capture devices 3027 while the rider 3044 is in the vehicle 3010, wherein the processing of the set of images is to produce the feature vectors 30130 of the image of the face of the rider. In embodiments, the transportation system further comprises: image capture devices 3027 disposed to capture a set of images of the face of the rider in the vehicle from a plurality of perspectives; and an image processing system to produce the feature vectors from the set of images captured from at least one of the plurality of perspectives.

[0663] In embodiments, the transportation system 3011 further comprises an interface 30133 between the first neural network and the image processing system 30132 to communicate a time sequence of the feature vectors, wherein the feature vectors are indicative of the emotional state of the rider. In embodiments, the feature vectors indicate at least one of a changing emotional state of the rider, a stable emotional state of the rider, a rate of change of the emotional state of the rider, a direction of change of the emotional state of the rider, a polarity of a change of the emotional state of the rider; the emotional state of the rider is changing to the unfavorable emotional state; and the emotional state of the rider is changing to the favorable emotional state.

[0664] In embodiments, the operational parameter that is optimized affects at least one of a route of the vehicle, in- vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, the second neural network is to interact with a vehicle control system to adjust the operational parameter. In embodiments, the artificial intelligence system further comprises a neural network that includes one or more perceptrons that mimic human senses that facilitates determining the emotional state of the rider based on an extent to which at least one of the senses of the rider is stimulated. In embodiments, the artificial intelligence system includes: a recurrent neural network to indicate a change in the emotional state of the rider through recognition of patterns of the feature vectors of the image of the face of the rider in the vehicle; and a radial basis function neural network to optimize, for achieving the favorable emotional state of the rider, the operational parameter of the vehicle in response to the indication of the change in the emotional state of the rider.

[0665] In embodiments, the radial basis function neural network is to optimize the operational parameter based on a correlation between a vehicle operating state and a rider emotional state. In embodiments, the operational parameter of the vehicle that is optimized is determined and adjusted to induce a favorable rider emotional state. In embodiments, the recurrent neural network further learns to classify the patterns of the feature vectors and associate the patterns of the feature vectors to emotional states and changes thereto from a training data set sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice system. In embodiments, the radial basis function neural network is to optimize the operational parameter in real time responsive to the detecting of the change in the emotional state of the rider by the Page 65 of 713SFT-108-A-PCT recurrent neural network. In embodiments, the recurrent neural network detects a pattern of the feature vectors that indicates the emotional state of the rider is changing from a first emotional state to a second emotional state. In embodiments, the radial basis function neural network is to optimize the operational parameter of the vehicle in response to the indicated change in emotional state.

[0666] In embodiments, the recurrent neural network comprises a plurality of connected nodes that form a directed cycle, the recurrent neural network further facilitating bi-directional flow of data among the connected nodes. In embodiments, the feature vectors indicate at least one of the emotional state of the rider is changing, the emotional state of the rider is stable, a rate of change of the emotional state of the rider, a direction of change of the emotional state of the rider, and a polarity of a change of the emotional state of the rider; the emotional state of a rider is changing to an unfavorable emotional state; and an emotional state of a rider is changing to a favorable emotional state. In embodiments, the operational parameter that is optimized affects at least one of a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route.

[0667] In embodiments, the radial basis function neural network is to interact with a vehicle control system 30134 to adjust the operational parameter 30124. In embodiments, the artificial intelligence system 3036 further comprises a neural network that includes one or more perceptrons that mimic human senses that facilitates determining the emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. In embodiments, the artificial intelligence system 3036 is to maintain the favorable emotional state of the rider via a modular neural network, the modular neural network comprising: a rider emotional state determining neural network to process the feature vectors of the image of the face of the rider in the vehicle to detect patterns. In embodiments, the patterns in the feature vectors indicate at least one of the favorable emotional state and the unfavorable emotional state; an intermediary circuit to convert data from the rider emotional state determining neural network into vehicle operational state data; and a vehicle operational state optimizing neural network to adjust an operational parameter of the vehicle in response to the vehicle operational state data.

[0668] In embodiments, the vehicle operational state optimizing neural network is to adjust the operational parameter 30124 of the vehicle for achieving a favorable emotional state of the rider. In embodiments, the vehicle operational state optimizing neural network is to optimize the operational parameter based on a correlation between a vehicle operating state 3045 and a rider emotional state 3066. In embodiments, the operational parameter of the vehicle that is optimized is determined and adjusted to induce a favorable rider emotional state. In embodiments, the rider emotional state determining neural network further learns to classify the patterns of the feature vectors and associate the pattern of the feature vectors to emotional states and changes thereto from a training data set sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice 3228 system.

[0669] In embodiments, the vehicle operational state optimizing neural network is to optimize the operational parameter 30124 in real time responsive to the detecting of a change in an emotional state 30126 of the rider by the rider emotional state determining neural network. In embodiments, the rider emotional state determining neural network is to detect a pattern of the feature vectors 30130 that indicates the emotional state of the rider is changing from a first emotional state to a second emotional state. In embodiments, the vehicle operational state optimizing neural network is to optimize the operational parameter of the vehicle in response to the indicated change in emotional state. In Page 66 of 713SFT-108-A-PCT embodiments, the artificial intelligence system 3036 comprises a plurality of connected nodes that form a directed cycle, the artificial intelligence system further facilitating bi-directional flow of data among the connected nodes.

[0670] In embodiments, the feature vectors 30130 indicate at least one of the emotional state of the rider is changing, the emotional state of the rider is stable, a rate of change of the emotional state of the rider, a direction of change of the emotional state of the rider, and a polarity of a change of the emotional state of the rider; the emotional state of a rider is changing to an unfavorable emotional state; and the emotional state of the rider is changing to a favorable emotional state. In embodiments, the operational parameter that is optimized affects at least one of a route of the vehicle, in-vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, the vehicle operational state optimizing neural network interacts with a vehicle control system to adjust the operational parameter.

[0671] In embodiments, the artificial intelligence system 3036 further comprises a neural net that includes one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. It is to be understood that the terms “neural net” and “neural network” are used interchangeably in the present disclosure. In embodiments, the rider emotional state determining neural network comprises one or more perceptrons that mimic human senses that facilitates determining an emotional state of a rider based on an extent to which at least one of the senses of the rider is stimulated. In embodiments, the artificial intelligence system 3036 includes a recurrent neural network to indicate a change in the emotional state of the rider in the vehicle through recognition of patterns of the feature vectors of the image of the face of the rider in the vehicle; the transportation system further comprising: a vehicle control system 30134 to control operation of the vehicle by adjusting a plurality of vehicle operational parameters 30124; and a feedback loop to communicate the indicated change in the emotional state of the rider between the vehicle control system 30134 and the artificial intelligence system 3036. In embodiments, the vehicle control system is to adjust at least one of the plurality of vehicle operational parameters 30124 in response to the indicated change in the emotional state of the rider. In embodiments, the vehicle controls system adjusts the at least one of the plurality of vehicle operational parameters based on a correlation between vehicle operational state and rider emotional state.

[0672] In embodiments, the vehicle control system adjusts the at least one of the plurality of vehicle operational parameters 30124 that are indicative of a favorable rider emotional state. In embodiments, the vehicle control system 30134 selects an adjustment of the at least one of the plurality of vehicle operational parameters 30124 that is indicative of producing a favorable rider emotional state. In embodiments, the recurrent neural network further learns to classify the patterns of feature vectors and associate them to emotional states and changes thereto from a training data set 30131 sourced from at least one of a stream of data from unstructured data sources, social media sources, wearable devices, in-vehicle sensors, a rider helmet, a rider headgear, and a rider voice system. In embodiments, the vehicle control system 30134 adjusts the at least one of the plurality of vehicle operation parameters 30124 in real time. In embodiments, the recurrent neural network detects a pattern of the feature vectors that indicates the emotional state of the rider is changing from a first emotional state to a second emotional state. In embodiments, the vehicle operation control system adjusts an operational parameter of the vehicle in response to the indicated change in emotional state. In embodiments, the recurrent neural network comprises a plurality of connected nodes that form a directed cycle, the recurrent neural network further facilitating bi-directional flow of data among the connected nodes.

[0673] In embodiments, the feature vectors indicating at least one of an emotional state of the rider is changing, an emotional state of the rider is stable, a rate of change of an emotional state of the rider, a direction of change of an Page 67 of 713SFT-108-A-PCT emotional state of the rider, and a polarity of a change of an emotional state of the rider; an emotional state of a rider is changing to an unfavorable state; an emotional state of a rider is changing to a favorable state. In embodiments, the at least one of the plurality of vehicle operational parameters responsively adjusted affects a route of the vehicle, in- vehicle audio content, speed of the vehicle, acceleration of the vehicle, deceleration of the vehicle, proximity to objects along the route, proximity to other vehicles along the route. In embodiments, the at least one of the plurality of vehicle operation parameters that is responsively adjusted affects operation of a powertrain of the vehicle and a suspension system of the vehicle. In embodiments, the radial basis function neural network interacts with the recurrent neural network via an intermediary component of the artificial intelligence system 3036 that produces vehicle control data indicative of an emotional state response of the rider to a current operational state of the vehicle. In embodiments, the recognition of patterns of feature vectors comprises processing the feature vectors of the image of the face of the rider captured during at least two of before the adjusting at least one of the plurality of vehicle operational parameters, during the adjusting at least one of the plurality of vehicle operational parameters, and after adjusting at least one of the plurality of vehicle operational parameters.

[0674] In embodiments, the adjusting at least one of the plurality of vehicle operational parameters 30124 improves an emotional state of a rider in a vehicle. In embodiments, the adjusting at least one of the plurality of vehicle operational parameters causes an emotional state of the rider to change from an unfavorable emotional state to a favorable emotional state. In embodiments, the change is indicated by the recurrent neural network. In embodiments, the recurrent neural network indicates a change in the emotional state of the rider responsive to a change in an operating parameter of the vehicle by determining a difference between a first set of feature vectors of an image of the face of a rider captured prior to the adjusting at least one of the plurality of operating parameters and a second set of feature vectors of an image of the face of the rider captured during or after the adjusting at least one of the plurality of operating parameters.

[0675] In embodiments, the recurrent neural network detects a pattern of the feature vectors that indicates an emotional state of the rider is changing from a first emotional state to a second emotional state. In embodiments, the vehicle operation control system adjusts an operational parameter of the vehicle in response to the indicated change in emotional state.

[0676] Referring to FIG.31, in embodiments, provided herein are transportation systems having an artificial intelligence system for processing a voice of a rider in a vehicle to determine an emotional state and optimizing at least one operating parameter of the vehicle to improve the rider’s emotional state. A voice-analysis module may take voice input and, using a training set of labeled data where individuals indicate emotional states while speaking and / or whether others tag the data to indicate perceived emotional states while individuals are talking, a machine learning system (such as any of the types described herein) may be trained (such as using supervised learning, deep learning, or the like) to classify the emotional state of the individual based on the voice. Machine learning may improve classification by using feedback from a large set of trials, where feedback in each instance indicates whether the system has correctly assessed the emotional state of the individual in the case of an instance of speaking. Once trained to classify the emotional state, an expert system (optionally using a different machine learning system or other artificial intelligence system) may, based on feedback of outcomes of the emotional states of a set of individuals, be trained to optimize various vehicle parameters noted throughout this disclosure to maintain or induce more favorable states. For example, among many other indicators, where a voice of an individual indicates happiness, the expert system may select or recommend upbeat music to maintain that state. Where a voice indicates stress, the system may recommend Page 68 of 713SFT-108-A-PCT or provide a control signal to change a planned route to one that is less stressful (e.g., has less stop-and-go traffic, or that has a higher probability of an on-time arrival). In embodiments, the system may be configured to engage in a dialog (such as on on-screen dialog or an audio dialog), such as using an intelligent agent module of the system, that is configured to use a series of questions to help obtain feedback from a user about the user’s emotional state, such as asking the rider about whether the rider is experiencing stress, what the source of the stress may be (e.g., traffic conditions, potential for late arrival, behavior of other drivers, or other sources unrelated to the nature of the ride), what might mitigate the stress (route options, communication options (such as offering to send a note that arrival may be delayed), entertainment options, ride configuration options, and the like), and the like. Driver responses may be fed as inputs to the expert system as indicators of emotional state, as well as to constrain efforts to optimize one or more vehicle parameters, such as by eliminating options for configuration that are not related to a driver’s source of stress from a set of available configurations.

[0677] An aspect provided herein includes a transportation system 3111, comprising: an artificial intelligence system 3136 for processing a voice 31135 of a rider 3144 in a vehicle 3110 to determine an emotional state 3166 of the rider 3144 and optimizing at least one operating parameter 31124 of the vehicle 3110 to improve the emotional state 3166 of the rider 3144.

[0678] An aspect provided herein includes an artificial intelligence system 3136 for voice processing to improve rider satisfaction in a transportation system 3111, comprising: a rider voice capture system 30136 deployed to capture voice output 31128 of a rider 3144 occupying a vehicle 3110; a voice-analysis circuit 31132 trained using machine learning that classifies an emotional state 31138 of the rider for the captured voice output of the rider; and an expert system 31139 trained using machine learning that optimizes at least one operating parameter 31124 of the vehicle to change the rider emotional state to an emotional state classified as an improved emotional state.

[0679] In embodiments, the rider voice capture system 31136 comprises an intelligent agent 3140 that engages in a dialog with the rider to obtain rider feedback for use by the voice-analysis circuit 31132 for rider emotional state classification. In embodiments, the voice-analysis circuit 31132 uses a first machine learning system and the expert system 31139 uses a second machine learning system. In embodiments, the expert system 31139 is trained to optimize the at least one operating parameter 31124 based on feedback of outcomes of the emotional states when adjusting the at least one operating parameter 31124 for a set of individuals. In embodiments, the emotional state 3166 of the rider is determined by a combination of the captured voice output 31128 of the rider and at least one other parameter. In embodiments, the at least one other parameter is a camera-based emotional state determination of the rider. In embodiments, the at least one other parameter is traffic information. In embodiments, the at least one other parameter is weather information. In embodiments, the at least one other parameter is a vehicle state. In embodiments, the at least one other parameter is at least one pattern of physiological data of the rider. In embodiments, the at least one other parameter is a route of the vehicle. In embodiments, the at least one other parameter is in-vehicle audio content. In embodiments, the at least one other parameter is a speed of the vehicle. In embodiments, the at least one other parameter is acceleration of the vehicle. In embodiments, the at least one other parameter is deceleration of the vehicle. In embodiments, the at least one other parameter is proximity to objects along the route. In embodiments, the at least one other parameter is proximity to other vehicles along the route.

[0680] An aspect provided herein includes an artificial intelligence system 3136 for voice processing to improve rider satisfaction, comprising: a first neural network 3122 trained to classify emotional states based on analysis of human voices detects an emotional state of a rider through recognition of aspects of the voice output 31128 of the rider Page 69 of 713SFT-108-A-PCT captured while the rider is occupying the vehicle 3110 that correlate to at least one emotional state 3166 of the rider; and a second neural network 3120 that optimizes, for achieving a favorable emotional state of the rider, an operational parameter 31124 of the vehicle in response to the detected emotional state 31126 of the rider 3144. In embodiments, at least one of the neural networks is a convolutional neural network. In embodiments, the first neural network 3122 is trained through use of a training data set that associates emotional state classes with human voice patterns. In embodiments, the first neural network 3122 is trained through the use of a training data set of voice recordings that are tagged with emotional state identifying data. In embodiments, the emotional state of the rider is determined by a combination of the captured voice output of the rider and at least one other parameter. In embodiments, the at least one other parameter is a camera-based emotional state determination of the rider. In embodiments, the at least one other parameter is traffic information. In embodiments, the at least one other parameter is weather information. In embodiments, the at least one other parameter is a vehicle state.

[0681] In embodiments, the at least one other parameter is at least one pattern of physiological data of the rider. In embodiments, the at least one other parameter is a route of the vehicle. In embodiments, the at least one other parameter is in-vehicle audio content. In embodiments, the at least one other parameter is a speed of the vehicle. In embodiments, the at least one other parameter is acceleration of the vehicle. In embodiments, the at least one other parameter is deceleration of the vehicle. In embodiments, the at least one other parameter is proximity to objects along the route. In embodiments, the at least one other parameter is proximity to other vehicles along the route.

[0682] Referring now to FIG.32, in embodiments provided herein are transportation systems 3211 having an artificial intelligence system 3236 for processing data from an interaction of a rider with an electronic commerce system of a vehicle to determine a rider state and optimizing at least one operating parameter of the vehicle to improve the rider’s state. Another common activity for users of device interfaces is e-commerce, such as shopping, bidding in auctions, selling items and the like. E-commerce systems use search functions, undertake advertising and engage users with various workflows that may eventually result in an order, a purchase, a bid, or the like. As described herein with search, a set of in-vehicle-relevant search results may be provided for e-commerce, as well as in-vehicle relevant advertising. In addition, in-vehicle-relevant interfaces and workflows may be configured based on detection of an in-vehicle rider, which may be quite different than workflows that are provided for e-commerce interfaces that are configured for smart phones or for desktop systems. Among other factors, an in-vehicle system may have access to information that is unavailable to conventional e-commerce systems, including route information (including direction, planned stops, planned duration and the like), rider mood and behavior information (such as from past routes, as well as detected from in-vehicle sensor sets), vehicle configuration and state information (such as make and model), and any of the other vehicle-related parameters described throughout this disclosure. As one example, a rider who is bored (as detected by an in-vehicle sensor set, such as using an expert system that is trained to detect boredom) and is on a long trip (as indicated by a route that is being undertaken by a car) may be far more patient, and likely to engage in deeper, richer content, and longer workflows, than a typical mobile user. As another example, an in-vehicle rider may be far more likely to engage in free trials, surveys, or other behaviors that promote brand engagement. Also, an in-vehicle user may be motivated to use otherwise down time to accomplish specific goals, such as shopping for needed items. Presenting the same interfaces, content, and workflows to in-vehicle users may miss excellent opportunities for deeper engagement that would be highly unlikely in other settings where many more things may compete for a user’s attention. In embodiments, an e-commerce system interface may be provided for in-vehicle users, where at least one of interface displays, content, search results, advertising, and one or more associated workflows (such as for shopping, bidding, Page 70 of 713SFT-108-A-PCT searching, purchasing, providing feedback, viewing products, entering ratings or reviews, or the like) is configured based on the detection of the use of an in-vehicle interface. Displays and interactions may be further configured (optionally based on a set of rules or based on machine learning), such as based on detection of display types (e.g., allowing richer or larger images for large, HD displays), network capabilities (e.g., enabling faster loading and lower latency by caching low-resolution images that initially render), audio system capabilities (such as using audio for dialog management and intelligence assistant interactions) and the like for the vehicle. Display elements, content, and workflows may be configured by machine learning, such as by A / B testing and / or using genetic programming techniques, such as configuring alternative interaction types and tracking outcomes. Outcomes used to train automatic configuration of workflows for in-vehicle e-commerce interfaces may include extent of engagement, yield, purchases, rider satisfaction, ratings, and others. In-vehicle users may be profiled and clustered, such as by behavioral profiling, demographic profiling, psychographic profiling, location-based profiling, collaborative filtering, similarity-based clustering, or the like, as with conventional e-commerce, but profiles may be enhanced with route information, vehicle information, vehicle configuration information, vehicle state information, rider information and the like. A set of in- vehicle user profiles, groups and clusters may be maintained separately from conventional user profiles, such that learning on what content to present, and how to present it, is accomplished with increased likelihood that the differences in in-vehicle shopping area accounted for when targeting search results, advertisements, product offers, discounts, and the like.

[0683] An aspect provided herein includes a transportation system 3211, comprising: an artificial intelligence system 3236 for processing data from an interaction of a rider 3244 with an electronic commerce system of a vehicle to determine a rider state 3249 and optimizing at least one operating parameter of the vehicle to improve the rider state.

[0684] An aspect provided herein includes a rider satisfaction system 3223 for optimizing rider satisfaction 3221, the rider satisfaction system comprising: an electronic commerce interface 3241 deployed for access by a rider in a vehicle 3210; a rider interaction circuit that captures rider interactions with the deployed interface 3241; a rider state determination circuit 3243 that processes the captured rider interactions 3244 to determine a rider state 3237; and an artificial intelligence system 3236 trained to optimize, responsive to a rider state 3237, at least one parameter 3224 affecting operation of the vehicle to improve the rider state 3237. In embodiments, the vehicle 3210 comprises a system for automating at least one control parameter of the vehicle. In embodiments, the vehicle is at least a semi-autonomous vehicle. In embodiments, the vehicle is automatically routed. In embodiments, the vehicle is a self-driving vehicle. In embodiments, the electronic commerce interface is self-adaptive and responsive to at least one of an identity of the rider, a route of the vehicle, a rider mood, rider behavior, vehicle configuration, and vehicle state.

[0685] In embodiments, the electronic commerce interface 3241 provides in-vehicle-relevant content 3246 that is based on at least one of an identity of the rider, a route of the vehicle, a rider mood, rider behavior, vehicle configuration, and vehicle state. In embodiments, the electronic commerce interface executes a user interaction workflow 3247 adapted for use by a rider 3244 in a vehicle 3210. In embodiments, the electronic commerce interface provides one or more results of a search query 3248 that are adapted for presentation in a vehicle. In embodiments, the search query results adapted for presentation in a vehicle are presented in the electronic commerce interface along with advertising adapted for presentation in a vehicle. In embodiments, the rider interaction circuit 3242 captures rider interactions 3244 with the interface responsive to content 3246 presented in the interface.

[0686] FIG. 33 illustrates a method 3300 for optimizing a parameter of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At 3302 the method includes capturing rider interactions with an in-vehicle Page 71 of 713SFT-108-A-PCT electronic commerce system. At 3304 the method includes determining a rider state based on the captured rider interactions and a least one operating parameter of the vehicle. At 3306 the method includes processing the rider state with a rider satisfaction model that is adapted to suggest at least one operating parameter of a vehicle the influences the rider state. At 3308 the method includes optimizing the suggested at least one operating parameter for at least one of maintaining and improving a rider state.

[0687] Referring to FIG.32 and FIG.33, an aspect provided herein includes an artificial intelligence system 3236 for improving rider satisfaction, comprising: a first neural network 3222 trained to classify rider states based on analysis of rider interactions 32144 with an in-vehicle electronic commerce system to detect a rider state 32149 through recognition of aspects of the rider interactions 32144 captured while the rider is occupying the vehicle that correlate to at least one state 3237 of the rider; and a second neural network 3220 that optimizes, for achieving a favorable state of the rider, an operational parameter of the vehicle in response to the detected state of the rider.

[0688] Referring to FIG. 34, in embodiments provided herein are transportation systems 3411 having an artificial intelligence system 3436 for processing data from at least one Internet of Things (IoT) device 3450 in the environment 3451 of a vehicle 3410 to determine a state 3452 of the vehicle and optimizing at least one operating parameter 3424 of the vehicle to improve a rider’s state 3437 based on the determined state 34152 of the vehicle.

[0689] An aspect provided herein includes a system for transportation 3411, comprising: an artificial intelligence system 3436 for processing data from at least one Internet of Things device 34150 in an environment 34151 of a vehicle 3410 to determine a determined state 3452 of the vehicle and optimizing at least one operating parameter 3424 of the vehicle to improve a state 3437 of the rider based on the determined state 3452 of the vehicle 3410.

[0690] FIG.35 illustrates a method 3500 for improving a state of a rider through optimization of operation of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At 3502 the method includes capturing vehicle operation-related data with at least one Internet-of-things device. At 3504 the method includes analyzing the captured data with a first neural network that determines a state of the vehicle based at least in part on a portion of the captured vehicle operation-related data. At 3506 the method includes receiving data descriptive of a state of a rider occupying the operating vehicle. At 3508 the method includes using a neural network to determine at least one vehicle operating parameter that affects a state of a rider occupying the operating vehicle. At 3509 the method includes using an artificial intelligence-based system to optimize the at least one vehicle operating parameter so that a result of the optimizing comprises an improvement in the state of the rider.

[0691] Referring to FIG.34 and FIG.35, in embodiments, the vehicle 3410 comprises a system for automating at least one control parameter 3453 of the vehicle 3410. In embodiments, the vehicle 3410 is at least a semi-autonomous vehicle. In embodiments, the vehicle 3410 is automatically routed. In embodiments, the vehicle 3410 is a self-driving vehicle. In embodiments, the at least one Internet-of-things device 3450 is disposed in an operating environment 3454 of the vehicle. In embodiments, the at least one Internet-of-things device 3450 that captures the data about the vehicle 3410 is disposed external to the vehicle 3410. In embodiments, the at least one Internet-of-things device is a dashboard camera. In embodiments, the at least one Internet-of-things device is a mirror camera. In embodiments, the at least one Internet-of-things device is a motion sensor. In embodiments, the at least one Internet-of-things device is a seat-based sensor system. In embodiments, the at least one Internet-of-things device is an IoT enabled lighting system. In embodiments, the lighting system is a vehicle interior lighting system. In embodiments, the lighting system is a headlight lighting system. In embodiments, the at least one Internet-of-things device is a traffic light camera or sensor. In embodiments, the at least one Internet-of-things device is a roadway camera. In embodiments, the roadway camera Page 72 of 713SFT-108-A-PCT is disposed on at least one of a telephone phone and a light pole. In embodiments, the at least one Internet-of-things device is an in-road sensor. In embodiments, the at least one Internet-of-things device is an in-vehicle thermostat. In embodiments, the at least one Internet-of-things device is a toll booth. In embodiments, the at least one Internet-of- things device is a street sign. In embodiments, the at least one Internet-of-things device is a traffic control light. In embodiments, the at least one Internet-of-things device is a vehicle mounted sensor. In embodiments, the at least one Internet-of-things device is a refueling system. In embodiments, the at least one Internet-of-things device is a recharging system. In embodiments, the at least one Internet-of-things device is a wireless charging station.

[0692] An aspect provided herein includes a rider state modification system 34155 for improving a state 3437 of a rider 3444 in a vehicle 3410, the system comprising: a first neural network 3422 that operates to classify a state of the vehicle through analysis of information about the vehicle captured by an Internet-of-things device 3450 during operation of the vehicle 3410; and a second neural network 3420 that operates to optimize at least one operating parameter 3424 of the vehicle based on the classified state 3452 of the vehicle, information about a state of a rider occupying the vehicle, and information that correlates vehicle operation with an effect on rider state.

[0693] In embodiments, the vehicle comprises a system for automating at least one control parameter 3453 of the vehicle 3410. In embodiments, the vehicle 3410 is at least a semi-autonomous vehicle. In embodiments, the vehicle 3410 is automatically routed. In embodiments, the vehicle 3410 is a self-driving vehicle. In embodiments, the at least one Internet-of-things device 34150 is disposed in an operating environment of the vehicle 3410. In embodiments, the at least one Internet-of-things device 3450 that captures the data about the vehicle 3410 is disposed external to the vehicle 3410. In embodiments, the at least one Internet-of-things device is a dashboard camera. In embodiments, the at least one Internet-of-things device is a mirror camera. In embodiments, the at least one Internet-of-things device is a motion sensor. In embodiments, the at least one Internet-of-things device is a seat-based sensor system. In embodiments, the at least one Internet-of-things device is an IoT enabled lighting system.

[0694] In embodiments, the lighting system is a vehicle interior lighting system. In embodiments, the lighting system is a headlight lighting system. In embodiments, the at least one Internet-of-things ...

Claims

1. SFT-108-A-PCT CLAIMS What is claimed: Configured artificial intelligence system 1. A configured artificial intelligence system for transportation comprising: a processor and memory configured to execute an intelligence system that provides intelligence service to an intelligence client in a transportation environment; an intelligence controller that acts as a control tower for the intelligence service and coordinates performance of a task; a model execution system that executes an artificial intelligence model for transportation decision-making; a data services system that processes transportation data; a security system that monitors the transportation data for adversarial attack; and a permissions system that controls access to the transportation intelligence service.

2. The system of claim 1, wherein the intelligence system includes a training and reinforcement system that maintains and trains the artificial intelligence model for transportation application.

3. The system of claim 1, wherein the intelligence system includes a governance and analysis system that analyzes the artificial intelligence model for transportation compliance.

4. The system of claim 1, wherein the intelligence system includes a scoring system that generates a score for transportation data quality.

5. The system of claim 1, wherein the intelligence system includes a model interface system that receives input indicating a transportation task.

6. The system of claim 1, wherein the artificial intelligence model comprises a large language model configured to process transportation-related text data.

7. The system of claim 1, wherein the artificial intelligence model comprises a multimodal model that processes transportation data across multiple format.

8. The system of claim 1, wherein the security system includes a know your data system that monitors input data provided to the artificial intelligence model for adversarial attack by analyzing pattern in real-time transportation data stream.

9. The system of claim 1, wherein the data services system processes sensor data from a transportation vehicle.

10. The system of claim 1, wherein the intelligence controller delegates a subtask to the artificial intelligence model for transportation decision-making.

11. The system of claim 1, wherein the permissions system controls access to transportation resource through the intelligence service.

12. The system of claim 1, further comprising a reporting system that generates a compliance report for the intelligence service in the transportation environment.

13. A configured artificial intelligence system for software defined vehicle comprising: a processor and memory configured to execute a know your model system that manages a lifecycle of an artificial intelligence model deployed in a software defined vehicle; Page 687 of 713 SFT-108-A-PCT the know your model system configured to perform a model intake and registration action associated with a candidate artificial intelligence model for vehicle application; the know your model system configured to perform a model evaluation action associated with the candidate artificial intelligence model; the know your model system configured to perform a model deployment action that integrates the candidate artificial intelligence model into a vehicle inference platform; and a model execution system that executes the deployed artificial intelligence model for vehicle control decision.

14. The system of claim 13, wherein the know your model system is configured to perform a model monitoring and observability action associated with the deployed artificial intelligence model.

15. The system of claim 13, wherein the know your model system is configured to perform a model updating and retraining action associated with the deployed artificial intelligence model.

16. The system of claim 13, wherein the model intake and registration action includes validating that the candidate artificial intelligence model complies with dataset licensing requirement for vehicle application.

17. The system of claim 13, wherein the model evaluation and risk assessment action includes determining regulatory alignment of the candidate artificial intelligence model with applicable regulatory organization.

18. The system of claim 13, wherein the model deployment action includes production integration that hosts the deployed artificial intelligence model behind a standardized endpoint for vehicle system access.

19. The system of claim 13, wherein the know your model system includes a distributed trust ledger component that immutably records onboarding step and operational event for the deployed artificial intelligence model.

20. The system of claim 13, wherein the model evaluation and risk assessment action includes automated environment validation of the candidate artificial intelligence model prior to vehicle deployment.

21. The system of claim 13, wherein the know your model system performs endpoint configuration testing on the candidate artificial intelligence model for vehicle integration.

22. The system of claim 13, wherein the know your model system performs security and privacy validation on the candidate artificial intelligence model as part of the model intake and registration action.

23. The system of claim 13, wherein the model deployment action includes automatic scaling of model inference based on demand from vehicle system client. Physical artificial intelligence system 24. A configured artificial intelligence system for physical artificial intelligence in transportation comprising: a processor and memory configured to execute a know your physical artificial intelligence system that manages onboarding and deployment of a physical artificial intelligence system within a transportation environment; the know your physical artificial intelligence system configured to perform discovery and authentication action for the physical artificial intelligence system upon initial connection to a transportation network; the know your physical artificial intelligence system configured to receive and process a capability declaration from the physical artificial intelligence system that describes supported artificial intelligence task for transportation; the know your physical artificial intelligence system configured to execute policy and compliance validation action by comparing the capability declaration against organizational governance policy; and Page 688 of 713 SFT-108-A-PCT the know your physical artificial intelligence system configured to perform contextual configuration and operational parameterization of the physical artificial intelligence system following compliance approval.

25. The system of claim 24, wherein the discovery and authentication action includes hardware attestation using trusted platform module to verify device integrity of the physical artificial intelligence system.

26. The system of claim 24, wherein the capability declaration includes a machine-readable manifest describing supported artificial intelligence task and hardware specification of the physical artificial intelligence system.

27. The system of claim 24, wherein the policy and compliance validation action utilizes a policy-as-code approach to assess compliance state of the physical artificial intelligence system.

28. The system of claim 24, wherein the contextual configuration includes transmission of environment map and dynamic scheduling parameter to the physical artificial intelligence system.

29. The system of claim 24, wherein the know your physical artificial intelligence system validates calibration and performance of sensor against known baseline for the physical artificial intelligence system.

30. The system of claim 24, wherein the know your physical artificial intelligence system provides secure firmware update to the physical artificial intelligence system.

31. The system of claim 24, wherein the know your physical artificial intelligence system aggregates telemetry data from the physical artificial intelligence system for fleet performance monitoring.

32. The system of claim 24, wherein the know your physical artificial intelligence system executes simulated trial involving the physical artificial intelligence system before real-world deployment.

33. The system of claim 24, wherein the know your physical artificial intelligence system includes integration with a digital twin system that generates a digital twin representation of the physical artificial intelligence system.

34. The system of claim 24, wherein the know your physical artificial intelligence system initiates a secure erasure process upon decommissioning of the physical artificial intelligence system.

35. A configured artificial intelligence system for transportation artificial intelligence agent comprising: a processor and memory configured to execute an intelligent agent system that provides an artificial intelligence agent for transportation task; the artificial intelligence agent including at least one artificial intelligence model configured to receive a user prompt related to transportation and to take action to fulfill the user prompt; the artificial intelligence agent configured to operate based on training that involves a training data set related to transportation and a training metric; the artificial intelligence agent entrusted with a set of resource including computational resource and data source for transportation decision-making; and an intelligence controller that observes, rationalizes, verifies, alters, and regulates the artificial intelligence agent to generate understanding of behavior of the artificial intelligence agent in transportation context.

36. The system of claim 35, wherein the artificial intelligence agent is configured to control physical action and interaction of a transportation device.

37. The system of claim 35, wherein the artificial intelligence agent operates autonomously with little supervision in a transportation environment.

38. The system of claim 35, wherein the intelligence controller requires the artificial intelligence agent to adopt interpretable artificial intelligence model for transportation decision transparency. Page 689 of 713 SFT-108-A-PCT 39. The system of claim 35, wherein the intelligence controller requires the artificial intelligence agent to log and preserve internal reasoning as stepwise progression from input to output for transportation task.

40. The system of claim 35, wherein the artificial intelligence agent is organized to support interpretability through modular design with bounded reasoning task for transportation application.

41. The system of claim 35, wherein the intelligence controller generates record that preserves and documents reasoning of the artificial intelligence agent for transportation decision.

42. The system of claim 35, wherein the intelligence controller generates notification and alert in response to discovering problem with the artificial intelligence agent in transportation context.

43. The system of claim 35, wherein an intelligence controller intervenes in current processing of the artificial intelligence agent to prevent error in transportation decision-making.

44. The system of claim 35, wherein the intelligence controller executes randomized inspection and obfuscated test of the artificial intelligence agent for transportation safety.

45. The system of claim 35, wherein the artificial intelligence agent includes access to a transportation tool that performs action in a transportation system.

46. A method for managing an artificial intelligence model in a transportation system, the method comprising: executing a configured artificial intelligence system; receiving, by the intelligence system, a candidate artificial intelligence model for deployment in a software defined vehicle; performing, by a know your model system within the intelligence system, a model intake and registration action on the candidate artificial intelligence model; executing, by the know your model system, a model evaluation action on the candidate artificial intelligence model; performing, by the know your model system, a model deployment action to deploy the candidate artificial intelligence model in the software defined vehicle; and monitoring, by the know your model system, the deployed artificial intelligence model during operation of the software defined vehicle.

47. The method of claim 46, wherein the model intake and registration action comprises validating that the candidate artificial intelligence model complies with a dataset licensing requirement.

48. The method of claim 46, wherein a model evaluation and risk assessment action comprises performing endpoint configuration testing on the candidate artificial intelligence model.

49. The method of claim 46, wherein a model evaluation and risk assessment action comprises performing security and privacy validation on the candidate artificial intelligence model.

50. The method of claim 46, wherein the know your model system comprises a transformer-based large language model fine-tuned for legal document classification.

51. The method of claim 46, wherein the model deployment action comprises performing automated environment validation of the candidate artificial intelligence model prior to production deployment.

52. The method of claim 46, wherein the monitoring comprises performing model monitoring and observability action on the deployed artificial intelligence model.

53. The method of claim 46, wherein the know your model system is configured to perform model updating and retraining action on the deployed artificial intelligence model. Page 690 of 713 SFT-108-A-PCT 54. The method of claim 46, wherein a digital twin system generates a digital twin representation of the candidate artificial intelligence model during the model intake and registration action.

55. The method of claim 46, wherein a model evaluation and risk assessment action comprises evaluating alignment and compliance of the candidate artificial intelligence model with a regulatory requirement.

56. The method of claim 46, wherein the know your model system incorporates a distributed trust ledger component to record an onboarding step and a policy validation.

57. The method of claim 46, further comprising performing, by the know your model system, a model decommissioning action on the deployed artificial intelligence model when the software defined vehicle is retired.

58. A method for authenticating a physical artificial intelligence system in a transportation environment, the method comprising: executing a configured artificial intelligence system comprising an intelligence system and a know your physical artificial intelligence system; receiving, by the know your physical artificial intelligence system, a discovery packet from a physical artificial intelligence device associated with a vehicle, wherein the discovery packet comprises at least one of a cryptographic identity credential, a device serial number, and a pre-provisioned public key infrastructure certificate; performing, by the know your physical artificial intelligence system, hardware attestation using a trusted platform module to verify device authenticity of the physical artificial intelligence device; establishing, by the know your physical artificial intelligence system, an encrypted operational session with the physical artificial intelligence device; issuing, by the know your physical artificial intelligence system, an ephemeral operational token to the physical artificial intelligence device; and provisioning, by the know your physical artificial intelligence system, a signed session certificate that allows the physical artificial intelligence device to interact with a system component of the configured artificial intelligence system.

59. The method of claim 58, wherein the discovery packet is broadcast via a secure networking protocol comprising mutual transport layer security.

60. The method of claim 58, wherein the hardware attestation uses a secure enclave cryptographic signature to verify device integrity.

61. The method of claim 58, wherein establishing the encrypted operational session comprises using a zero-trust network onboarding standard.

62. The method of claim 58, wherein the signed session certificate allows the physical artificial intelligence device to interact with an orchestration component of the configured artificial intelligence system.

63. The method of claim 58, wherein the signed session certificate allows the physical artificial intelligence device to interact with a backend service of the configured artificial intelligence system.

64. The method of claim 58, further comprising receiving, by the know your physical artificial intelligence system, a comprehensive capability declaration from the physical artificial intelligence device after authentication.

65. The method of claim 58, wherein the cryptographic identity credential comprises a pre-provisioned public key infrastructure certificate. Page 691 of 713 SFT-108-A-PCT 66. The method of claim 58, wherein the ephemeral operational token has a limited validity period for enhanced security.

67. The method of claim 58, further comprising validating, by the know your physical artificial intelligence system, the device serial number against a registered device database.

68. The method of claim 58, wherein the physical artificial intelligence device comprises a mobile robot integrated into the vehicle.

69. The method of claim 58, wherein the physical artificial intelligence device comprises an autonomous surveillance unit integrated into the vehicle.

70. A method for validating capability of a physical artificial intelligence system in a vehicle, the method comprising: executing a configured artificial intelligence system comprising a know your physical artificial intelligence system; receiving, by the know your physical artificial intelligence system, a capability declaration from a physical artificial intelligence device integrated into a vehicle, wherein the capability declaration comprises a machine-readable manifest describing at least one of a supported artificial intelligence task, a hardware specification, an operational constraint, and a certified artificial intelligence model signature; parsing, by the know your physical artificial intelligence system, the capability declaration against an enterprise-wide capability ontology; validating, by the know your physical artificial intelligence system, the capability declaration; storing, by the know your physical artificial intelligence system, the capability declaration within a centralized dynamic system registry; and performing, by the know your physical artificial intelligence system, a policy check based on the stored capability declaration.

71. The method of claim 70, wherein the supported artificial intelligence task comprises object detection.

72. The method of claim 70, wherein the supported artificial intelligence task comprises obstacle avoidance.

73. The method of claim 70, wherein the supported artificial intelligence task comprises semantic segmentation.

74. The method of claim 70, wherein the hardware specification comprises a light detection and ranging sensor model.

75. The method of claim 70, wherein the hardware specification comprises a manipulator degree of freedom specification.

76. The method of claim 70, wherein the operational constraint comprises a maximum operating temperature.

77. The method of claim 70, wherein the operational constraint comprises a permissible indoor and outdoor transition specification.

78. The method of claim 70, wherein the certified artificial intelligence model signature comprises a secure hash algorithm 256 hash of neural network weight. Page 692 of 713 SFT-108-A-PCT 79. The method of claim 70, wherein the capability declaration further comprises battery management detail.

80. The method of claim 70, wherein the centralized dynamic system registry comprises a graph-based digital twin representation.

81. The method of claim 70, wherein the policy check comprises validating the capability declaration against an organizational policy and an external regulation. Artificial intelligence model interaction 82. A method for orchestrating artificial intelligence model interaction in a vehicle system, the method comprising: executing a configured artificial intelligence system comprising an artificial intelligence orchestrator system; establishing, by the artificial intelligence orchestrator system, a connection between a first artificial intelligence model and a second artificial intelligence model deployed in a vehicle; initiating, by the artificial intelligence orchestrator system, a session between the first artificial intelligence model and the second artificial intelligence model; exchanging, by the artificial intelligence orchestrator system, an authentication token and an identity token between the first artificial intelligence model and the second artificial intelligence model; performing, by the artificial intelligence orchestrator system, capability recognition wherein the first artificial intelligence model declares a capability and a limitation; and performing, by the artificial intelligence orchestrator system, input preparation and negotiation to format prepared input based on the declared capability.

83. The method of claim 82, wherein the connection establishment comprises a secure handshake between the first artificial intelligence model and the second artificial intelligence model.

84. The method of claim 82, wherein a secure handshake comprises application programming interface key validation.

85. The method of claim 82, wherein a secure handshake comprises an OAuth-like flow.

86. The method of claim 82, wherein a secure handshake comprises cryptographic signature verification.

87. The method of claim 82, wherein the capability comprises a supported input type and a supported output type.

88. The method of claim 82, wherein the capability comprises a supported language specification.

89. The method of claim 82, wherein the limitation comprises a performance constraint.

90. The method of claim 82, wherein the limitation comprises a latency constraint.

91. The method of claim 82, wherein the limitation comprises a policy restriction.

92. The method of claim 82, wherein the input preparation and negotiation comprises ensuring agreement on a data schema.

93. The method of claim 82, wherein the input preparation and negotiation comprises ensuring agreement on a tokenization specification and an encoding specification.

94. A method for generating a digital twin of a physical artificial intelligence system in a vehicle, the method comprising: Page 693 of 713 SFT-108-A-PCT executing a configured artificial intelligence system comprising a digital twin system and a know your physical artificial intelligence system; receiving, by the know your physical artificial intelligence system, discovery information from a physical artificial intelligence device in a vehicle; authenticating, by the know your physical artificial intelligence system, the physical artificial intelligence device using hardware attestation; generating, by the digital twin system, a digital twin representation of the physical artificial intelligence device based on the discovery information; receiving, by the know your physical artificial intelligence system, a capability declaration from the physical artificial intelligence device; updating, by the digital twin system, the digital twin representation with capability information from the capability declaration; and continuously updating, by the digital twin system, the digital twin representation using data streamed from the physical artificial intelligence device.

95. The method of claim 94, wherein the discovery information comprises a cryptographic identity credential, a device serial number, and a pre-provisioned public key infrastructure certificate.

96. The method of claim 94, wherein the hardware attestation uses a trusted platform module cryptographic signature.

97. The method of claim 94, wherein the capability declaration comprises a machine-readable manifest describing a supported artificial intelligence task.

98. The method of claim 94, wherein the capability declaration comprises a hardware specification and an operational constraint.

99. The method of claim 94, wherein the capability declaration comprises a certified artificial intelligence model signature.

100. The method of claim 94, wherein the digital twin representation models intelligent behavior of the physical artificial intelligence device.

101. The method of claim 94, wherein a digital twin representation models physical component and capability of the physical artificial intelligence device.

102. The method of claim 94, wherein the data streamed from the physical artificial intelligence device comprises validation result and policy configuration.

103. The method of claim 94, wherein continuously updating the digital twin representation comprises reflecting a current state and a compliance status of the physical artificial intelligence device.

104. The method of claim 94, wherein the digital twin representation comprises an operational history of the physical artificial intelligence device.

105. The method of claim 94, further comprising using, by the know your physical artificial intelligence system, simulation data involving the digital twin representation as input to an artificial intelligence-based learning model. Artificial intelligence model management Page 694 of 713 SFT-108-A-PCT 106. A computer-implemented method for managing artificial intelligence models in a transportation system, comprising: executing, by a processor, at least one model of a first set of AI-based learning models to perform model intake actions associated with a second set of AI-based learning models for transportation applications; performing, by the at least one model, a model evaluation action on the second set of AI-based learning models to assess a safety implication for a vehicular environment; executing a model deployment action to integrate the second set of AI-based learning models into transportation infrastructure; implementing a model monitoring action to track performance of a deployed model in a real-time transportation scenario; and performing a model update based on a new traffic pattern.

107. The method of claim 106, wherein the first set of AI-based learning models comprises transformer-based large language models for legal document classification to ensure compliance with a dataset licensing requirement.

108. The method of claim 106, wherein the model evaluation includes validation of transportation regulatory compliance and assessment of safety implications for autonomous vehicle operations.

109. The method of claim 106, wherein the AI-based learning models include at least one of a convolutional neural network, recurrent neural network, or transformer model configured for transportation application.

110. The method of claim 106, further comprising processing model documentation, training dataset metadata, and license agreement texts during an intake and registration process.

111. The method of claim 106, wherein the method outputs at least one of a compliance score, pass / fail status, or reasoning trace for model validation.

112. The method of claim 106, further comprising implementing security and privacy validation using multi- agent security validation systems.

113. The method of claim 106, wherein the model monitoring includes tracking model performance degradation in a transportation context.

114. The method of claim 106, further comprising generating a digital twin representation of AI models subject to an intake and registration process.

115. The method of claim 106, wherein the transportation applications include at least one of autonomous vehicle control, traffic management, or fleet coordination.

116. The method of claim 106, further comprising maintaining data provenance tracking throughout AI model lifecycles.

117. The method of claim 106, wherein the method includes human-in-the-loop evaluation for periodic assessment of model outputs.

118. A computer-implemented method for validating data integrity in transportation systems, comprising: analyzing, by a processor, a deviation across historical transportation datasets to identify an anomaly indicative of malicious manipulation in at least one of vehicle sensor outputs, traffic monitoring data, or transportation system logs; processing data received from a client input channel; implementing protocol-specific preprocessing; Page 695 of 713 SFT-108-A-PCT correlating IP geolocation data with a device attestation credential to prevent a spoofing attack by requiring spatial consistency between a network routing path and a hardware provenance marker; and implementing a dynamic data score adjustment based on a threat intelligence update.

119. The method of claim 118, wherein the historical datasets include vehicle performance records, traffic sensor data, and transportation infrastructure status information.

120. The method of claim 118, further comprising processing file upload interfaces receiving CSV exports from transportation databases.

121. The method of claim 118, wherein the method implements transportation-specific data privacy measures including anonymization of vehicle tracking data.

122. The method of claim 118, further comprising ensuring compliance with transportation privacy regulations and vehicle data protection standards.

123. The method of claim 118, wherein an adaptive filtering mechanism reduces computational overhead compared to static rule-based systems.

124. The method of claim 118, further comprising enabling efficient processing of high-velocity data streams in distributed architectures.

125. The method of claim 118, wherein the method integrates cryptographic authentication checks directly into content analysis pipelines.

126. The method of claim 118, further comprising maintaining data integrity across heterogeneous IoT ecosystems while preserving interoperability.

127. The method of claim 118, wherein the client input channels include vehicle telemetry systems and traffic monitoring infrastructure.

128. The method of claim 118, further comprising implementing real-time correlation of network routing data with hardware authentication credentials.

129. The method of claim 118, wherein the method addresses technical challenges in legacy industrial control system integration.

130. A computer-implemented method for optimizing powertrain performance using artificial intelligence, comprising: controlling, by an artificial intelligence system, a powertrain component based on an operational model selected from at least one of a physics model, electrodynamic model, hydrodynamic model, chemical models for energy conversion, or mechanical models for dynamically interacting system components; manipulating a powertrain operating parameter to achieve a desired powertrain state; training the AI system on datasets of outcomes including at least one of fuel efficiency, safety, or rider satisfaction; implementing a hybrid neural network wherein one neural network optimizes a gear shifting operation while another neural network optimizes at least one of a braking, clutch engagement, or energy discharge and recharging; and generating a control instruction consisting of output from at least one component of the hybrid neural network to control a powertrain component.

131. The method of claim 130, wherein the AI system is trained on datasets of operator actions sensed by sensor sets, cameras, or vehicle information systems. Page 696 of 713 SFT-108-A-PCT 132. The method of claim 130, wherein the hybrid neural network optimizes distinct parts of the powertrain using at least two separate neural network components.

133. The method of claim 130, further comprising processing social data from multiple social data sources to optimize powertrain operating states.

134. The method of claim 130, wherein the method processes data sourced from unstructured data sources and wearable devices.

135. The method of claim 130, further comprising processing data sourced from in-vehicle sensors and rider helmets.

136. The method of claim 130, wherein the operational models include predictive models for vehicle maintenance and remaining useful life estimation.

137. The method of claim 130, further comprising implementing classification models to predict failure within given time windows.

138. The method of claim 130, wherein the method includes regression models trained to predict remaining useful life of vehicle components.

139. The method of claim 130, further comprising collecting training data from vehicle specifications, environmental data, sensor data, and operational information.

140. The method of claim 130, wherein the AI system stores predictive models in a model datastore within a database system.

141. The method of claim 130, further comprising training multiple predictive models to answer different questions regarding powertrain maintenance and optimization.

142. A computer-implemented method for managing transportation system digital twins, comprising: receiving, by a processor, imported data from one or more data sources corresponding to a transportation system; generating a digital twin of the transportation system representing the system based on the imported data; identifying one or more transportation entities within the transportation system; generating a set of discrete digital twins representing the transportation entities within the transportation system; embedding the set of discrete digital twins within the digital twin of the transportation system; establishing a connection with a sensor system of the transportation system; receiving real-time sensor data from one or more sensors via the connection; and updating at least one of the digital twin of the transportation system and the set of discrete digital twins based on the real-time sensor data.

143. The method of claim 142, wherein the transportation entities include vehicles, infrastructure components, and traffic management systems.

144. The method of claim 142, further comprising implementing executive digital twins for vehicle fleet operations and vehicle digital twins for design and simulation.

145. The method of claim 142, wherein the method creates enterprise access layers for fleet transactions and comprehensive fleet management.

146. The method of claim 142, further comprising implementing contextual simulation and forecasting capabilities through enterprise layers. Page 697 of 713 SFT-108-A-PCT 147. The method of claim 142, wherein the method enables modeling of fleet operations, vehicle design scenarios, and enterprise-level transactions.

148. The method of claim 142, further comprising leveraging simulations to predict outcomes of operational configurations and optimize resource allocation.

149. The method of claim 142, wherein the method forecasts maintenance needs through continuous learning and adaptation.

150. The method of claim 142, further comprising refining predictive models based on actual outcomes to improve future simulation accuracy.

151. The method of claim 142, wherein the sensor system includes LIDAR, cameras, radar, GPS, and vehicle telemetry sensors.

152. The method of claim 143, further comprising implementing smart contract systems embedded in digital twins for liability management.

153. The method of claim 142, wherein the method includes interface systems for designating supervisors for AI systems represented in digital twins.

154. A computer-implemented method for implementing AI agent orchestration in transportation systems, comprising: deploying, by a processor, an AI agent for transportation applications including at least one of an autonomous vehicle control agent, traffic management agent, or fleet coordination assistant; configuring the AI agent with transportation-specific data; implementing AI orchestration for a transportation environment to enable AI-to-AI and AI-to-machine interactions between at least one of a vehicle systems, traffic infrastructure, or transportation AI models; facilitating coordination between different AI systems to provide transportation services; reorganizing resources based on changes in demand for processing by AI systems including new types of requests and processing of new data types; and determining a route between AI systems based on evaluation of a request requirement and corresponding features of candidate communication paths.

155. The method of claim 154, wherein the AI agents are configured with knowledge of traffic regulations, vehicle operation manuals, and transportation safety protocols.

156. The method of claim 154, further comprising reorganizing resources based on changes in available and feasible AI systems including large language models and hybrid systems.

157. The method of claim 154, wherein the method allocates increased computation and storage for models of increased size and computational complexity.

158. The method of claim 154, further comprising determining routes based on priority, deadline, data amount, budget, and security considerations.

159. The method of claim 154, wherein the communication paths include Bluetooth, Wi-Fi, cellular, infrared, and wired Ethernet connections.

160. The method of claim 154, further comprising evaluating candidate routes based on throughput, availability, cost, reliability, and security features.

161. The method of claim 154, wherein the method acquires, purchases, develops, reserves, and provisions communication routes between AI systems. Page 698 of 713 SFT-108-A-PCT 162. The method of claim 154, further comprising reserving communication paths between AI systems intended for high-performance communication.

163. The method of claim 154, wherein the orchestration handles low-latency AI system processing requests in real-time contexts.

164. The method of claim 154, further comprising processing new types of data including LIDAR point-cloud data through orchestrated AI systems.

165. The method of claim 154, wherein the method manages computational loads and request volumes through dynamic resource allocation. Multimodal processing 166. A computer-implemented method for processing multimodal transportation data, comprising: processing, by multimodal systems, data across multiple formats relevant to transportation including at least one of LIDAR point clouds, camera feeds, radar data, GPS coordinates, vehicle telemetry, traffic sensor data, or route optimization algorithms; enabling situational awareness by analyzing diverse sensor inputs simultaneously; operating foundation and multimodal models designed to process multiple input types, unifying vision, text, and audio under a single architecture; processing at least one of a vehicular sensor data, traffic communications, or navigation instructions in real- time; and creating a transportation-specific data model utilizing at least one of data stores, databases, data warehouses, or data lakes.

167. The method of claim 166, wherein the multimodal systems integrate vision, text, and audio processing capabilities for comprehensive transportation analysis.

168. The method of claim 166, further comprising implementing context-aware sensor fusion to inform analytics and AI processing.

169. The method of claim 166, wherein the method supports APIs and service-oriented architecture for distributed data processing.

170. The method of claim 166, further comprising processing sensor and energy operations data, market data, environmental data, and alternative data sources.

171. The method of claim 166, wherein data layer AI capabilities focus on sensor and data fusion for holistic understanding of vehicle operations.

172. The method of claim 166, further comprising leveraging machine learning algorithms to analyze transportation networks and optimize vehicle operations.

173. The method of claim 166, wherein the algorithms include graph neural networks that capture spatial structure of transportation networks.

174. The method of claim 166, further comprising implementing reinforcement learning to optimize routing decisions in real-time.

175. The method of claim 166, wherein the method uses clustering algorithms to segment data based on traffic patterns and geographic features. Page 699 of 713 SFT-108-A-PCT 176. The method of claim 166, further comprising implementing time-series forecasting models to predict future conditions based on historical data.

177. The method of claim 166, wherein a multi-faceted approach enables analysis of vehicle movement patterns and infrastructure utilization.

178. A computer-implemented method for implementing cognitive charging plans for vehicle fleets, comprising: processing, by an artificial intelligence system, inputs including predicted traffic conditions for a plurality of vehicles; exchanging information between cloud-based and vehicle-based systems about at least one of vehicle energy consumption, operational information, or recharging infrastructure; responding to transportation system and vehicle information with a control parameter that facilitates executing a cognitive charging plan for charging infrastructure; determining at least one charging plan parameter for at least a portion of the plurality of vehicles is dependent; and executing a program derived based on the charging plan by a processor.

179. The method of claim 178, wherein the inputs include predicted energy consumption patterns and charging infrastructure availability.

180. The method of claim 178, further comprising optimizing charging schedules based on electricity pricing and grid demand patterns.

181. The method of claim 178, wherein the cognitive charging plan considers vehicle route optimization and destination requirements.

182. The method of claim 178, further comprising coordinating charging activities across multiple vehicles to minimize grid impact.

183. The method of claim 178, wherein the method implements dynamic adjustment of charging parameters based on real-time conditions.

184. The method of claim 178, further comprising integrating weather data and traffic predictions into charging plan optimization.

185. The method of claim 178, wherein the cloud-based systems provide centralized coordination and the vehicle-based systems provide local optimization.

186. The method of claim 178, further comprising implementing predictive algorithms for traffic prediction and transportation prediction.

187. The method of claim 178, wherein the method includes energy calculation algorithms for optimizing fuel usage and electricity usage.

188. The method of claim 178, further comprising optimizing refueling or recharging time, location, and amount based on the charging plan.

189. The method of claim 178, wherein the method implements vehicle routing algorithms sensitive to vehicle operating parameters and user experience. Automated governance for transportation 190. A computer-implemented method for automated governance of transportation operations, comprising: Page 700 of 713 SFT-108-A-PCT implementing, by a processor, policy automation for a vehicle operation within a transportation system; executing regulatory compliance automation to ensure adherence to a transportation regulation; performing reporting automation for oversight of integrated transportation systems; providing automated governance through at least one of a policy enforcement, compliance monitoring, or digital rights management; and adapting a governance model to a detected regulatory requirement change.

191. The method of claim 190, wherein the policy automation includes enforcement of safety protocols and operational procedures.

192. The method of claim 190, further comprising monitoring compliance with vehicle data protection standards and privacy regulations.

193. The method of claim 190, wherein the regulatory compliance automation adapts to jurisdiction-specific transportation requirements.

194. The method of claim 190, further comprising generating automated reports for regulatory authorities and fleet management.

195. The method of claim 190, wherein the method implements digital rights management for transportation data and AI model usage.

196. The method of claim 190, further comprising providing governance across vehicle operations, traffic management, and infrastructure systems.

197. The method of claim 190, wherein an intelligent governance system learns from operational outcomes to improve policy effectiveness.

198. The method of claim 190, further comprising coordinating governance across disconnected technology infrastructure layers.

199. The method of claim 190, wherein the method reduces manual oversight requirements through automated policy enforcement.

200. The method of claim 190, further comprising implementing governance for AI system generation, training, verification, and deployment.

201. The method of claim 190, wherein an oversight capability extends across enterprise operations and technological resources.

202. A computer-implemented method for vehicle routing optimization using artificial intelligence, comprising: implementing, by a processor, a vehicle routing algorithm sensitive to at least one of vehicle operating parameters or user experience parameters; processing a traffic prediction algorithm and transportation prediction algorithm to optimize a routing decision; implementing an object detection algorithm for real-time route adjustment based on an environmental condition; calculating an energy parameter for at least one of optimizing fuel usage, electricity usage, refueling time, location, or amount; and providing a user satisfaction algorithm that calculates a rider preference in a routing decision. Page 701 of 713 SFT-108-A-PCT 203. The method of claim 202, wherein the vehicle operating parameters include powertrain efficiency, battery state, and mechanical system status.

204. The method of claim 202, further comprising processing user experience parameters including comfort preferences, time constraints, and destination priorities.

205. The method of claim 202, wherein a genetic algorithm evolves routing solutions based on multiple optimization criteria simultaneously.

206. The method of claim 202, further comprising implementing real-time object detection for dynamic route modification based on obstacles and hazards.

207. The method of claim 202, wherein the energy calculation algorithms consider vehicle-specific consumption patterns and charging infrastructure.

208. The method of claim 202, further comprising optimizing routes for multiple vehicles simultaneously to reduce overall system energy consumption.

209. The method of claim 202, wherein the traffic prediction algorithms use historical data and real-time sensor information.

210. The method of claim 202, further comprising implementing machine learning models that adapt routing strategies based on observed outcomes.

211. The method of claim 202, wherein the method balances multiple objectives including time, energy efficiency, safety, and user satisfaction.

212. The method of claim 202, further comprising providing dynamic route updates based on changing traffic conditions and infrastructure status.

213. The method of claim 202, wherein the user satisfaction algorithms learn individual preferences and adapt routing recommendations accordingly.

214. A computer-implemented method for AI system generation in transportation environments, comprising: generating, by an AI system generation module, an artificial intelligence system including at least one of a neural networks, machine learning models, or expert systems for a transportation application; provisionally reserving storage capacity for resources of artificial intelligence systems that may be generated in the future; storing resources for generating artificial intelligence systems including at least one of interpretable scripts, compliable source code repositories, executable code modules, and declarative hyperparameter sets; curating a stored artificial intelligence system based on generating a new system to replace a less performant system; configuring generative AI systems to create content for a transportation application and; and generating AI systems that operate independently and together to present transportation-related content.

215. The method of claim 214, wherein the neural networks include convolutional neural networks for image processing and recurrent neural networks for sequential data.

216. The method of claim 214, further comprising generating expert systems with domain-specific knowledge of transportation regulations and safety protocols.

217. The method of claim 214, wherein the storage reservation includes capacity planning for different types of AI models and their computational requirements. Page 702 of 713 SFT-108-A-PCT 218. The method of claim 214, further comprising maintaining version control and dependency management for stored AI system resources.

219. The method of claim 214, wherein a curation process includes performance monitoring and automated replacement of underperforming systems.

220. The method of claim 214, further comprising implementing discriminator networks that identify artifacts indicating synthetic content provenance.

221. The method of claim 214, wherein the generative AI systems create training data, documentation, and operational procedures for transportation systems.

222. The method of claim 214, further comprising configuring AI systems to alter content based on review feedback and generate replacement content.

223. The method of claim 214, wherein the method supports retraining of AI systems to address issues identified during content review.

224. The method of claim 214, further comprising generating combinations of AI systems that collaborate on complex transportation planning tasks.

225. The method of claim 214, wherein the AI system generation adapts to emerging transportation technologies and changing operational requirements.

226. A computer-implemented method for physical AI system integration in transportation, comprising: performing, by a processor, automated onboarding and deployment of a physical AI systems within a transportation system of systems environment; enabling secure AI-powered physical devices including at least one of a mobile robot, autonomous surveillance unit, intelligent delivery vehicle, or aerial drone; implementing discovery and authentication actions for physical AI systems upon initial connection; broadcasting discovery packets including at least one of cryptographic identity credentials, device serial numbers, and pre-provisioned public key infrastructure certificates; performing hardware attestation using at least one of trusted platform modules or secure enclave cryptographic signatures to verify device integrity and authenticity; and establishing an encrypted operational session.

227. The method of claim 226, wherein the physical AI systems include autonomous vehicles, traffic monitoring devices, and infrastructure control systems.

228. The method of claim 226, further comprising implementing mutual TLS or zero-trust network onboarding standards for secure communication.

229. The method of claim 226, wherein the discovery packets are transmitted via secure networking protocols with cryptographic verification.

230. The method of claim 226, further comprising validating device certificates against trusted certificate authorities and revocation lists.

231. The method of claim 226, wherein the hardware attestation includes verification of firmware integrity and hardware security modules.

232. The method of claim 226, further comprising implementing device identity management and lifecycle tracking for physical AI systems. Page 703 of 713 SFT-108-A-PCT 233. The method of claim 226, wherein the encrypted operational sessions use ephemeral keys and forward secrecy protocols.

234. The method of claim 226, further comprising integrating with know your model systems to manage AI model lifecycles for physical devices.

235. The method of claim 226, wherein the method enables secure interaction with orchestration components and backend services.

236. The method of claim 226, further comprising implementing policy enforcement for physical AI system operations and data access.

237. The method of claim 226, wherein a context-aware integration considers environmental conditions and operational requirements.

238. A computer-implemented method for AI model interpretability in transportation systems, comprising: organizing, by a configured artificial intelligence system, an AI agent to utilize AI models that facilitate interpretability over those that do not; using the AI agent to adopt interpretable AI models including at least one of shallow artificial neural networks, decision trees, or convolutional neural networks; preserving metadata relating to AI agent actions in a context of given inputs and corresponding outputs; and generating a documentation of the AI agent’s actions and outputs as at least one of narrative descriptions, flowcharts, or interpretable AI models.

239. The method of claim 238, wherein the interpretable AI models are specifically configured for vehicle control and traffic management decisions.

240. The method of claim 238, further comprising generating decision trees that explain routing choices and safety-critical decisions.

241. The method of claim 238, wherein a stepwise reasoning preservation includes timestamps and decision context for audit trails.

242. The method of claim 238, further comprising maintaining reasoning traces for regulatory compliance and accident investigation.

243. The method of claim 238, wherein an explanation include natural language descriptions of AI decision- making processes.

244. The method of claim 238, further comprising generating visual flowcharts that illustrate AI reasoning paths for human operators.

245. The method of claim 238, wherein the interpretability requirements are tailored to transportation safety standards and regulations.

246. The method of claim 238, further comprising implementing explainable AI techniques for autonomous vehicle decision validation.

247. The method of claim 238, wherein the method supports real-time explanation generation for critical transportation decisions.

248. The method of claim 238, further comprising providing interpretability interfaces for transportation system operators and regulators.

249. The method of claim 238, wherein the interpretable models maintain performance while providing transparency for safety-critical applications. Page 704 of 713 SFT-108-A-PCT Cross-service resource optimization 250. A computer-implemented method for cross-service resource optimization in transportation systems, comprising: managing, by a cross-service resource optimization system comprising AI agents, configurable integration capabilities across internal subsystems and services of a transportation platform; learning interfaces of subsystems from different platforms and generating data and network connections among them; generating interfaces by which users or AI agents may manage data and network connections between integrated subsystems; integrating data and network communication, data storage, management interfaces, and energy resources across platforms; translating isolated platforms into converged platforms through parallel connectivity across internal subsystems; and evolving from pairwise converged platforms to converged platforms that encompass subsystem elements across integrated platforms.

251. The method of claim 250, wherein the transportation platforms include vehicle control systems, traffic management systems, and fleet coordination platforms.

252. The method of claim 250, further comprising implementing AI agents trained to understand platform- specific interfaces and communication protocols.

253. The method of claim 250, wherein the data and network connections enable real-time information sharing between previously isolated systems.

254. The method of claim 250, further comprising providing unified management interfaces for controlling integrated transportation subsystems.

255. The method of claim 250, wherein the energy resource integration optimizes power distribution across connected transportation platforms.

256. The method of claim 250, further comprising implementing scalable integration that supports addition of new transportation platforms.

257. The method of claim 250, wherein a massively parallel connectivity enables simultaneous operation of multiple transportation services.

258. The method of claim 250, further comprising managing configuration, deployment, provisioning, and optimization of integrated subsystems.

259. The method of claim 250, wherein the converged platform maintains operational efficiency while expanding integration capabilities.

260. The method of claim 250, further comprising implementing security measures that protect integrated systems while enabling seamless communication.

261. The method of claim 250, wherein an evolution to full convergence maintains backward compatibility with existing transportation systems.

262. A computer-implemented method for transportation system learning and adaptation, comprising: Page 705 of 713 SFT-108-A-PCT providing an AI convergence system of systems to track operational data and outcomes of a transportation system; implementing a contextual simulation and forecasting capabilities through enterprise layers to enable modeling at least one of fleet operations, vehicle design scenarios, and enterprise-level transactions; and predicting outcomes based on the simulation and forecasting relating to at least one of operational configurations, optimize resource allocation, and forecast maintenance needs.

263. The method of claim 262, wherein the operational data includes vehicle performance metrics, traffic patterns, and user behavior analytics.

264. The method of claim 262, further comprising implementing continuous learning algorithms that adapt to changing transportation environments.

265. The method of claim 262, wherein a new technology adoption includes integration of emerging sensors, communication protocols, and AI models.

266. The method of claim 262, further comprising maintaining system scalability through cloud-based and distributed computing architectures.

267. The method of claim 262, wherein a modular design enables independent updates and enhancements to system components.

268. The method of claim 262, further comprising implementing version control and compatibility management for system modules.

269. The method of claim 262, wherein an adaptive learning mechanism includes reinforcement learning and transfer learning techniques.

270. The method of claim 262, further comprising refining predictive models based on actual outcomes to improve future simulation accuracy.

271. The method of claim 262, wherein the contextual simulation considers environmental conditions, regulatory changes, and market dynamics.

272. The method of claim 262, further comprising implementing feedback loops that enable continuous system improvement based on performance metrics. Data layer 273. The method of claim 262, wherein the forecasting capabilities support proactive maintenance scheduling and resource planning.

274. A computer-implemented method for transportation data analysis and modeling, comprising: implementing, by a processor, a data layer providing context-aware sensor fusion data; processing at least one of sensor data, energy operations data, market data, environmental data, and alternative data sources through a service-oriented architecture; analyzing a transportation network using a machine learning algorithm including a graph neural network related to a spatial structure of a transportation network; implementing reinforcement learning to optimize a routing decision in real-time; implementing a clustering algorithm to segment data based on at least one of a traffic pattern or geographic feature; utilizing a time-series forecasting model to predict a future condition based on historical data; and Page 706 of 713 SFT-108-A-PCT enabling analysis of vehicle movement patterns.

275. The method of claim 274, wherein the context-aware sensor fusion combines data from LIDAR, cameras, radar, GPS, and vehicle telemetry systems.

276. The method of claim 274, further comprising implementing distributed data processing capabilities for handling high-volume transportation data streams.

277. The method of claim 274, wherein the graph neural networks model complex relationships between transportation network nodes and edges.

278. The method of claim 274, further comprising using reinforcement learning agents that learn optimal routing policies through interaction with traffic environments.

279. The method of claim 274, wherein the clustering algorithms identify patterns in vehicle behavior, traffic flow, and infrastructure usage.

280. The method of claim 274, further comprising implementing time-series models that account for seasonal variations and trend analysis in transportation data.

281. The method of claim 274, wherein a multi-faceted analysis combines statistical methods, machine learning, and domain expertise.

282. The method of claim 274, further comprising providing real-time analytics capabilities for immediate decision-making support.

283. The method of claim 274, wherein the method supports both batch processing and stream processing of transportation data.

284. The method of claim 274, further comprising implementing data quality assessment and anomaly detection for transportation datasets.

285. The method of claim 274, wherein the analysis results inform policy decisions, infrastructure planning, and operational optimization.

286. A computer-implemented method for safety assurance in autonomous transportation systems, comprising: integrating, by a processor, know your data (KYD) and know your model (KYM) capabilities to provide safety assurance for autonomous vehicles and transportation infrastructure; validating incoming sensor and communication data through the KYD system while confirming AI models processing the data meet safety and performance standards through the KYM system; implementing AI-enabled data provenance engines and associated rules that provide tracking, validation, and governance of data throughout AI and machine learning system lifecycles for transportation applications; implementing mechanisms for regular human-in-the-loop evaluation and external audits where human reviewers and domain experts periodically assess model outputs for subtle degradations; maintaining data provenance tracking where an intelligence system logs detailed metadata on data origin, source credibility, and whether data instances are model-generated or human-produced; and preventing degradation of transportation AI models through comprehensive monitoring and validation processes.

287. The method of claim 286, wherein the safety assurance includes validation of decision-making accuracy and assessment of safety implications for vehicular environments. Page 707 of 713 SFT-108-A-PCT 288. The method of claim 286, further comprising implementing real-time monitoring of AI model performance in safety-critical transportation scenarios.

289. The method of claim 286, wherein the KYD system validates sensor data integrity and detects anomalies in vehicle telemetry and traffic monitoring data.

290. The method of claim 286, further comprising ensuring KYM system compliance with transportation regulatory requirements and safety standards.

291. The method of claim 286, wherein the data provenance engines track data lineage from sensors through processing pipelines to decision outputs.

292. The method of claim 286, further comprising implementing governance rules that enforce data quality standards and model performance thresholds.

293. The method of claim 286, wherein the human-in-the-loop evaluation includes assessment of factual accuracy, semantic richness, and safety performance.

294. The method of claim 286, further comprising conducting external audits by transportation safety experts and regulatory authorities.

295. The method of claim 286, wherein the data provenance tracking includes timestamps, processing history, and quality metrics for all data instances.

296. The method of claim 286, further comprising implementing automated alerts and corrective actions when model degradation is detected.

297. The method of claim 286, wherein the comprehensive monitoring includes performance metrics, safety indicators, and compliance status tracking.

298. A transportation artificial intelligence module system comprising: an AI agent including at least one of an autonomous vehicle control agent, traffic management agent, or fleet coordination assistant configured with transportation-specific knowledge; a know your model (KYM) system for managing AI models in vehicular environments that performs model intake and registration for transportation AI models; and a governance module enforcing at least one governance rule related to a transportation regulatory standard.

299. The system of claim 298, wherein the autonomous vehicle control agents process real-time sensor data for navigation decisions.

300. The system of claim 298, wherein the traffic management agents optimize traffic flow and incident response.

301. The system of claim 298, wherein the fleet coordination assistants manage multi-vehicle operations and scheduling.

302. The system of claim 298, wherein the KYM system ensures transportation AI models meet NHTSA safety standards.

303. The system of claim 298, wherein a model intake process includes validation of transportation-specific performance metrics.

304. The system of claim 298, wherein a safety validation framework includes testing under various driving conditions. Page 708 of 713 SFT-108-A-PCT 305. The system of claim 298, wherein the regulatory compliance monitoring ensures adherence to DOT guidelines.

306. The system of claim 298, wherein a traffic safety oversight includes real-time monitoring of AI decision- making.

307. The system of claim 298, wherein the AI agents adapt to different transportation environments and conditions.

308. The system of claim 298, wherein the KYM system validates AI models for deployment in safety-critical applications.

309. The system of claim 298, wherein the governance modules implement automated compliance checking.

310. The system of claim 298, wherein the system ensures AI models operate within defined safety parameters.

311. The system of claim 298, wherein the transportation-specific knowledge includes traffic laws and vehicle operation protocols.

312. The system of claim 298, wherein the AI agents coordinate with human operators for optimal transportation outcomes.

313. The system of claim 298, wherein the KYM system provides continuous monitoring of AI model performance in vehicles.

314. The system of claim 298, wherein a governance framework adapts to changing transportation regulations.

315. The system of claim 298, wherein the system supports both individual vehicle and fleet-level AI operations.

316. The system of claim 298, wherein the AI modules integrate with existing transportation management systems.

317. A transportation governance layer for AI convergence systems comprising: governance module storing transportation regulations including at least one of DOT guidelines, NHTSA safety standards, or international vehicle safety protocols; AI governor monitoring actions of AI system during both training and inference phases; and a policy engine that serves as a central control mechanism that defines, stores, and enforces governance policies across the AI system specifically tailored for transportation safety requirements.

318. The system of claim 317, wherein the governance modules implement data privacy governance for vehicle and passenger data protection.

319. The system of claim 317, wherein the system implements differential privacy mechanisms that add calibrated noise to AI / ML model outputs.

320. The system of claim 317, wherein the policy engine enforces governance policies during AI system training phases.

321. The system of claim 317, wherein the AI governors monitor AI system behavior during inference phases.

322. The system of claim 317, wherein a specialized governance module ensures compliance with DOT guidelines.

323. The system of claim 317, wherein the system enforces NHTSA safety standards across transportation AI systems. Page 709 of 713 SFT-108-A-PCT 324. The system of claim 317, wherein the governance policies are specifically tailored for autonomous vehicle operations.

325. The system of claim 317, wherein the control mechanisms regulate AI behavior in real-time transportation scenarios.

326. The system of claim 317, wherein the policy engine stores governance policies in a centralized repository.

327. The system of claim 317, wherein the system defines governance policies for different transportation modalities.

328. The system of claim 317, wherein the governance modules prevent individual record identification while preserving analytical utility.

329. The system of claim 317, wherein the system implements regulation governance modules for vehicle data protection.

330. The system of claim 317, wherein the AI governors constrain AI system behavior based on safety requirements.

331. The system of claim 317, wherein a governance framework supports traffic pattern analysis and transportation research.

332. The system of claim 317, wherein the policy engine adapts to changing regulatory requirements automatically.

333. The system of claim 317, wherein the specialized governance modules integrate with existing transportation management systems.

334. The system of claim 317, wherein the system maintains compliance across multiple jurisdictional requirements.

335. The system of claim 317, wherein the governance policies support both cloud-based and edge-deployed AI systems.

336. A transportation offering system comprising: location-based offering system providing contextual services based on a vehicle position and environmental condition; advertising integration for targeted content delivery within transportation environments; and a customer-facing vehicle digital twin providing personalized transportation experiences and real-time vehicle status information to users based on the vehicle position and environmental condition.

337. The system of claim 336, wherein the location-based services adapt to changing environmental conditions.

338. The system of claim 336, wherein the advertising integration provides contextually relevant content to vehicle occupants.

339. The system of claim 336, wherein the customer-facing digital twins provide real-time vehicle performance data.

340. The system of claim 336, wherein the location-based offerings include route-specific services and recommendations.

341. The system of claim 336, wherein a marketplace integration enables in-vehicle commerce and service booking. Page 710 of 713 SFT-108-A-PCT 342. The system of claim 336, wherein the digital twins provide personalized transportation experiences based on user preferences.

343. The system of claim 336, wherein the system delivers targeted content based on vehicle location and user profile.

344. The system of claim 336, wherein the customer-facing services include real-time traffic and navigation updates.

345. The system of claim 336, wherein the location-based system provides contextual information about nearby services.

346. The system of claim 336, wherein an advertising platform integrates with vehicle entertainment systems.

347. The system of claim 336, wherein the digital twins enable remote vehicle monitoring and control capabilities.

348. The system of claim 336, wherein an offering layer customizes services based on transportation patterns and preferences.

349. The system of claim 336, wherein the system provides location-aware safety alerts and warnings.

350. The system of claim 336, wherein a marketplace integration supports mobility-as-a-service transactions.

351. The system of claim 336, wherein the customer-facing interfaces provide intuitive vehicle interaction capabilities.

352. The system of claim 336, wherein the location-based services integrate with smart city infrastructure.

353. The system of claim 336, wherein the system enables personalized content delivery based on travel context.

354. The system of claim 336, wherein an offering layer supports both individual and shared transportation services.

355. A transportation transactions layer system comprising: processor for data transfer between vehicles, infrastructure, and transportation systems, wherein the data transfer supports digital transactions including at least one of vehicle-to-everything (V2X) communication protocols, traffic data sharing, and interoperability with external transportation networks; API gateway managing data ingress points for transportation data, and serving as enforcement node for at least one of safety compliance or vehicle data validation; and governance module for cryptographic validation of data sources to confirm vehicle sensor data integrity and authenticity.

356. The system of claim 355, wherein the V2X communication protocols enable secure inter-vehicle data exchange.

357. The system of claim 355, wherein the API gateways implement cryptographic validation for transportation data sources.

358. The system of claim 355, wherein the system ensures traffic data sharing integrity across transportation networks.

359. The system of claim 355, wherein a cryptographic attestation prevents unauthorized access to vehicle sensor data.

360. The system of claim 355, wherein the transaction system supports interoperability with external transportation platforms. Page 711 of 713 SFT-108-A-PCT 361. The system of claim 355, wherein the data validation includes real-time verification of vehicle communications.

362. The system of claim 355, wherein the API gateways enforce safety compliance for all transportation data exchanges.

363. The system of claim 355, wherein the system manages secure communication channels for vehicle data transmission.

364. The system of claim 355, wherein the V2X protocols support emergency vehicle communication prioritization.

365. The system of claim 355, wherein the cryptographic requirements ensure data authenticity throughout transmission.

366. The system of claim 355, wherein the transaction layer enables secure integration with traffic management systems.

367. The system of claim 355, wherein the system provides tamper-evident data exchange for transportation safety.

368. The system of claim 355, wherein the API gateways support high-throughput transportation data processing.

369. The system of claim 355, wherein a secure exchange includes vehicle-to-infrastructure communication protocols.

370. The system of claim 355, wherein the system maintains data integrity across distributed transportation networks.

371. The system of claim 355, wherein the cryptographic attestation includes hardware-based security validation.

372. The system of claim 355, wherein the transaction layer supports real-time data validation for autonomous vehicles.

373. The system of claim 355, wherein the system enables secure data sharing between different transportation authorities. Page 712 of 713

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