Digital twin system and method for transport systems

Abstract

We provide a digital twin system and method for transportation systems. [Solution] The method includes the steps of: receiving a request to update one or more transport system digital twins; obtaining one or more transport system digital twins from a digital twin data store to satisfy the request; obtaining one or more dynamic models from a dynamic model data store to satisfy the request; selecting data sources from a set of available data sources for one or more inputs to one or more dynamic models; obtaining data from the selected data sources; running one or more dynamic models using the obtained data as input data to determine one or more output values; and updating one or more properties of one or more transport system digital twins based on one or more output values ​​of one or more dynamic models.

Description

Technical Field

[0001] (Cross - Reference to Related Applications) This application claims priority to U.S. Provisional Patent Application No. 63 / 016,973, entitled "Digital Twin Systems And Methods For Transportation Systems," filed on April 28, 2020, and U.S. Provisional Patent Application No. 63 / 054,609, entitled "Digital Twin Systems And Methods For Transportation Systems," filed on July 21, 2020, each of which is hereby incorporated by reference in its entirety as if fully set forth herein.

[0002] (Technical Field) The present disclosure relates to an intelligent digital twin system for creating, managing, and providing a digital twin of a transportation system using sensor data and the like.

Background Art

[0003] (Background) A digital twin is a digital information construct related to machines, physical devices, systems, processes, people, etc. Once created, a digital twin can be used to represent a machine as a digital representation of a real - world system. A digital twin is created to have the same shape and behavior as the corresponding machine. Further, a digital twin can reflect the state of a machine within a larger system. For example, sensors can be installed on a machine to obtain real - time (or near - real - time) data from a physical object and relay it to a remote digital twin.

[0004] A digital twin can be used to simulate or mimic the operation of machines and physical devices within a virtual world. In that case, the digital twin can display the structural components of a machine, show the life cycle and / or design steps, and can be displayed via a user interface.

[0005] The proliferation of sensors, networks, and communication technologies in transportation systems generates vast amounts of data. This data is useful for predicting maintenance needs or classifying potential problems in transportation systems. However, there are many untapped applications for sensor data in transportation systems to improve their operation and uptime, and to enable transportation entities to respond quickly to situations.

[0006] Transportation companies that rely on subject matter experts may struggle to retain the expertise of these experts when they move to another company or leave their jobs. In such technical fields, there is a need to retain subject matter expertise and to use that expertise to guide new workers or mobile electronic transport entities in performing transport service-related tasks. [Overview of the Initiative]

[0007] (overview) In particular, provided herein are methods, systems, components, processes, modules, blocks, circuits, subsystems, articles, and other elements (which may be collectively referred to as "platforms" or "systems" in some cases, and these terms should be understood to encompass either of the above unless otherwise specified in the context).

[0008] This specification provides methods and systems for updating the properties of digital twins of transport entities and transport systems, according to some embodiments of the present disclosure. These methods and systems are based on the influence of vibration data collected on a set of digital twin dynamic models, such that the digital twin provides a computer-generated representation of the transport entity or system, for example, but are not limited to the following.

[0009] According to some embodiments of the present disclosure, a method for updating one or more properties of one or more transport system digital twins is disclosed. The method for updating one or more properties of one or more digital twins includes the steps of: receiving a request to update one or more properties of one or more digital twins; retrieving one or more digital twins from a digital twin data store as necessary to satisfy the request; retrieving one or more dynamic models from a dynamic model data store as necessary to satisfy the request; selecting data sources from a set of available data sources for one or more inputs to one or more dynamic models; retrieving data from the selected data sources; running one or more dynamic models using the retrieved data as input data and determining one or more output values; and updating one or more properties of one or more digital twins based on one or more output values ​​of one or more dynamic models.

[0010] In this embodiment, the request is received from a client application corresponding to a transport system or one or more transport entities within the transport system.

[0011] In this embodiment, requests are received from a client application that supports a network-connected sensor system.

[0012] In this embodiment, the request is received from a client application that supports the vibration sensor system.

[0013] In one embodiment, one or more transport system digital twins include one or more digital twins of transport entities.

[0014] In one embodiment, one or more dynamic models take in data selected from a set consisting of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, sound, video, images, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biocompound concentration, metal concentration, and organic compound concentration data.

[0015] In this embodiment, the selected data source is chosen from a set consisting of analog vibration sensors, digital vibration sensors, fixed digital vibration sensors, 3-axis vibration sensors, 1-axis vibration sensors, optical vibration sensors, switches, network-connected devices, and machine vision systems.

[0016] In one embodiment, retrieving one or more dynamic models involves identifying one or more dynamic models based on one or more properties indicated in the request and the respective types of one or more transport system digital twins.

[0017] In the embodiment, one or more dynamic models are identified using a lookup table.

[0018] In this embodiment, the digital twin dynamic model system acquires data from a selected data source via a digital twin I / O system.

[0019] According to some embodiments of the present disclosure, a method for updating one or more bearing vibration failure level states of one or more transport system digital twins is disclosed. The method includes the steps of: receiving a request from a client application to update one or more bearing vibration failure level states of one or more digital twins; retrieving one or more digital twins from a digital twin data store necessary to satisfy the request; retrieving one or more dynamic models from a dynamic model data store necessary to satisfy the request; selecting data sources from a set of available data sources for one or more inputs to one or more dynamic models; retrieving data from the selected data sources; running one or more dynamic models using the retrieved data as input data and calculating output values ​​representing one or more bearing vibration failure level states; and updating one or more bearing vibration failure level states of one or more digital twins based on the output values ​​of one or more dynamic models.

[0020] In the embodiment, one or more bearing vibration failure level states are selected from the group consisting of normal, below optimal, critical, and alarm.

[0021] In this embodiment, the client application corresponds to a transport system or one or more transport entities within the transport system.

[0022] In this embodiment, the client application supports a sensor system connected to a network.

[0023] In this embodiment, the client application supports the vibration sensor system.

[0024] In one embodiment, one or more transport system digital twins include one or more digital twins of transport entities.

[0025] In an embodiment, one or more dynamic models capture data selected from a set consisting of vibration, temperature, pressure, humidity, wind, rainfall, tide, high tide, cloud cover, snowfall, visibility, radiation, audio, video, images, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biocompound concentration, metal concentration, and organic compound concentration data.

[0026] In an embodiment, the selected data source is selected from a set consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a three-axis vibration sensor, a one-axis vibration sensor, an optical vibration sensor, a switch, a network connection device, and a machine vision system.

[0027] In an embodiment, searching for one or more dynamic models includes identifying one or more dynamic models based on a request and each type of one or more transportation system digital twins.

[0028] In an embodiment, one or more dynamic models are identified using a lookup table.

[0029] In an embodiment, the digital twin dynamic model system obtains data from the selected data source via a digital twin I / O system.

[0030] According to some embodiments of the present disclosure, a method is disclosed for updating one or more vibration intensity unit values ​​of one or more transport system digital twins. The method includes: receiving a request from a client application to update one or more vibration intensity unit values ​​of one or more transport system digital twins; retrieving one or more transport system digital twins from a digital twin data store as necessary to satisfy the request; retrieving one or more dynamic models from a dynamic model data store as necessary to satisfy the request; selecting data sources from a set of available data sources for one or more inputs to one or more of the one or more dynamic models; retrieving data from the selected data sources; running one or more dynamic models using the retrieved data as inputs and calculating one or more output values ​​representing one or more vibration intensity unit values; and updating one or more vibration intensity unit values ​​of one or more transport system digital twins based on one or more output values ​​of the one or more dynamic models.

[0031] In this embodiment, the unit of vibration intensity represents displacement.

[0032] In this embodiment, the unit of vibration intensity represents velocity.

[0033] In this embodiment, the unit of vibration intensity is acceleration.

[0034] In this embodiment, the client application corresponds to a transport system or one or more transport entities within the transport system.

[0035] In this embodiment, the client application supports a sensor system connected to a network.

[0036] In this embodiment, the client application supports the vibration sensor system.

[0037] In one embodiment, one or more transport system digital twins include one or more digital twins of transport entities.

[0038] In one embodiment, one or more dynamic models take in data selected from a set consisting of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, sound, video, images, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biocompound concentration, metal concentration, and organic compound concentration data.

[0039] In this embodiment, the selected data source is chosen from the group consisting of analog vibration sensors, digital vibration sensors, fixed digital vibration sensors, 3-axis vibration sensors, 1-axis vibration sensors, optical vibration sensors, switches, network-connected devices, and machine vision systems.

[0040] In one embodiment, retrieving one or more dynamic models involves identifying one or more dynamic models based on the requirements and the respective types of one or more transport system digital twins.

[0041] In the embodiment, one or more dynamic models are identified using a lookup table.

[0042] In this embodiment, the digital twin dynamic model system acquires data from a selected data source via a digital twin I / O system.

[0043] According to some embodiments of the present disclosure, a method is disclosed for updating one or more failure probability values ​​of one or more transport system digital twins. The method includes the steps of: receiving a request from a client application to update one or more failure probability values ​​of one or more transport system digital twins; acquiring one or more transport system digital twins to satisfy the request; acquiring one or more dynamic models to satisfy the request; selecting data sources from a set of available data sources for one or more inputs to the one or more dynamic models; acquiring data from the selected data sources; running one or more dynamic models with the acquired data as inputs and calculating one or more output values ​​representing one or more failure probability values; and updating one or more failure probability values ​​of the one or more transport system digital twins based on one or more output values ​​of the one or more dynamic models.

[0044] In this embodiment, the client application corresponds to a transport system or one or more transport entities within the transport system.

[0045] In this embodiment, the client application supports a sensor system connected to a network.

[0046] In this embodiment, the client application supports the vibration sensor system.

[0047] In one embodiment, one or more transport system digital twins include one or more digital twins of transport entities.

[0048] In one embodiment, one or more dynamic models take in data selected from a set consisting of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, sound, video, images, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biocompound concentration, metal concentration, and organic compound concentration data.

[0049] In this embodiment, the selected data source is chosen from a set consisting of analog vibration sensors, digital vibration sensors, fixed digital vibration sensors, 3-axis vibration sensors, 1-axis vibration sensors, optical vibration sensors, switches, network-connected devices, and machine vision systems.

[0050] In one embodiment, retrieving one or more dynamic models involves identifying one or more dynamic models based on the requirements and the respective types of one or more transport system digital twins.

[0051] In the embodiment, one or more dynamic models are identified using a lookup table.

[0052] In this embodiment, the digital twin dynamic model system acquires data from a selected data source via a digital twin I / O system.

[0053] According to some embodiments of the present disclosure, a method for updating one or more downtime probability values ​​of one or more transport system digital twins is disclosed. The method includes the steps of: receiving a request to update one or more downtime probability values ​​of one or more transport system digital twins; retrieving one or more transport system digital twins from a digital twin data store to satisfy the request; retrieving one or more dynamic models from a dynamic model data store to satisfy the request; selecting data sources from a set of available data sources for one or more inputs of one or more dynamic models; retrieving data from the selected data sources; running one or more dynamic models using the retrieved data as inputs and calculating one or more output values ​​representing one or more downtime probability values; and updating one or more downtime probability values ​​of one or more transport system digital twins based on one or more output values ​​of one or more dynamic models.

[0054] In this embodiment, the request is received from a client application corresponding to a transport system or one or more transport entities within the transport system.

[0055] In this embodiment, requests are received from a client application that supports a network-connected sensor system.

[0056] In this embodiment, the request is received from a client application that supports the vibration sensor system.

[0057] In one embodiment, one or more transport system digital twins include one or more digital twins of transport entities.

[0058] In one embodiment, one or more dynamic models take in data selected from a set consisting of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, sound, video, images, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biocompound concentration, metal concentration, and organic compound concentration data.

[0059] In this embodiment, the selected data source is chosen from a set consisting of analog vibration sensors, digital vibration sensors, fixed digital vibration sensors, 3-axis vibration sensors, 1-axis vibration sensors, optical vibration sensors, switches, network-connected devices, and machine vision systems.

[0060] In one embodiment, retrieving one or more dynamic models involves identifying one or more dynamic models based on the requirements and the respective types of one or more transport system digital twins.

[0061] In the embodiment, one or more dynamic models are identified using a lookup table.

[0062] In this embodiment, the digital twin dynamic model system acquires data from a selected data source via a digital twin I / O system.

[0063] According to some embodiments of the present disclosure, a method for updating one or more shutdown probability values ​​for one or more transport system digital twins having a set of transport entities is disclosed. The method includes: receiving a request from a client application to update one or more shutdown probability values ​​for a set of transport entities in one or more transport system digital twins; retrieving one or more transport system digital twins from a digital twin data store to satisfy the request; retrieving one or more dynamic models from a dynamic model data store to satisfy the request; selecting data sources from a set of available data sources for one or more inputs to one or more dynamic models; retrieving data from the selected data sources; running one or more dynamic models using the retrieved data as inputs and calculating one or more output values ​​representing one or more shutdown probability values; and updating one or more shutdown probability values ​​for a set of transport entities in one or more transport system digital twins based on one or more output values ​​of the one or more dynamic models.

[0064] In this embodiment, the client application corresponds to a transport system or one or more transport entities within the transport system.

[0065] In this embodiment, the client application supports a sensor system connected to a network.

[0066] In this embodiment, the client application supports the vibration sensor system.

[0067] In one embodiment, one or more transport system digital twins include one or more digital twins of transport entities.

[0068] In this embodiment, the set of transport entities includes a refueling center or a vehicle recharging center.

[0069] In one embodiment, one or more dynamic models take in data selected from a set consisting of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, sound, video, images, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biocompound concentration, metal concentration, and organic compound concentration data.

[0070] In this embodiment, the selected data source is chosen from a set consisting of analog vibration sensors, digital vibration sensors, fixed digital vibration sensors, 3-axis vibration sensors, 1-axis vibration sensors, optical vibration sensors, switches, network-connected devices, and machine vision systems.

[0071] In one embodiment, retrieving one or more dynamic models involves identifying one or more dynamic models based on the requirements and the respective types of one or more transport system digital twins.

[0072] In the embodiment, one or more dynamic models are identified using a lookup table.

[0073] In this embodiment, the digital twin dynamic model system acquires data from a selected data source via a digital twin I / O system.

[0074] According to some embodiments of the present disclosure, a method for updating one or more downtime cost values ​​of one or more transport system digital twins is disclosed. The method includes the steps of: receiving a request to update one or more downtime cost values ​​of one or more transport system digital twins; retrieving one or more transport system digital twins from a digital twin data store to satisfy the request; retrieving one or more dynamic models from a dynamic model data store to satisfy the request; selecting data sources from a set of available data sources for one or more inputs to one or more of the one or more dynamic models; retrieving data from the selected data sources; running one or more dynamic models with the retrieved data as inputs and calculating one or more output values ​​representing one or more downtime cost values; and updating one or more downtime cost values ​​of one or more transport system digital twins based on one or more output values ​​of the one or more dynamic models.

[0075] In one embodiment, the downtime cost value is selected from a set of downtime costs per hour, per day, per week, per month, per quarter, and per year.

[0076] In this embodiment, the request is received from a client application corresponding to a transport system or one or more transport entities within the transport system.

[0077] In this embodiment, requests are received from a client application that supports a network-connected sensor system.

[0078] In this embodiment, the request is received from a client application that supports the vibration sensor system.

[0079] In one embodiment, one or more transport system digital twins include one or more digital twins of transport entities.

[0080] In one embodiment, one or more dynamic models take in data selected from a set consisting of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, sound, video, images, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biocompound concentration, metal concentration, and organic compound concentration data.

[0081] In this embodiment, the selected data source is chosen from a set consisting of analog vibration sensors, digital vibration sensors, fixed digital vibration sensors, 3-axis vibration sensors, 1-axis vibration sensors, optical vibration sensors, switches, network-connected devices, and machine vision systems.

[0082] In one embodiment, retrieving one or more dynamic models involves identifying one or more dynamic models based on the requirements and the respective types of one or more transport system digital twins.

[0083] In the embodiment, one or more dynamic models are identified using a lookup table.

[0084] In this embodiment, the digital twin dynamic model system acquires data from a selected data source via a digital twin I / O system.

[0085] According to some embodiments of the present disclosure, a method is disclosed for updating one or more key performance indicator (KPI) values ​​of one or more transport system digital twins. The method includes the steps of: receiving a request to update one or more key performance indicator values ​​of one or more transport system digital twins; retrieving one or more transport system digital twins from a digital twin data store to satisfy the request; retrieving one or more dynamic models from a dynamic model data store to satisfy the request; selecting data sources from a set of data sources available for one or more inputs to one or more dynamic models; retrieving data from the selected data sources; running one or more dynamic models using the retrieved data as inputs and calculating one or more output values ​​representing one or more key performance indicator values; and updating one or more key performance indicator values ​​of one or more transport system digital twins based on one or more output values ​​of one or more dynamic models.

[0086] In the embodiment, the key performance indicators are selected from a set of operating hours, utilization rate, standard operating efficiency, overall operating efficiency, overall equipment effectiveness, machine downtime, unplanned downtime, machine setup time, on-time delivery, training time, employee turnover rate, reportable health and safety incidents, revenue per employee, profit per employee, schedule completion, planned maintenance rate, and utilization rate.

[0087] In this embodiment, the request is received from a client application corresponding to a transport system or one or more transport entities within the transport system.

[0088] In this embodiment, requests are received from a client application that supports a network-connected sensor system.

[0089] In this embodiment, the request is received from a client application that supports the vibration sensor system.

[0090] In one embodiment, one or more transport system digital twins include one or more digital twins of transport entities.

[0091] In one embodiment, one or more dynamic models take in data selected from a set consisting of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, sound, video, images, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biocompound concentration, metal concentration, and organic compound concentration data.

[0092] In this embodiment, the selected data source is chosen from a set consisting of analog vibration sensors, digital vibration sensors, fixed digital vibration sensors, 3-axis vibration sensors, 1-axis vibration sensors, optical vibration sensors, switches, network-connected devices, and machine vision systems.

[0093] In one embodiment, retrieving one or more dynamic models involves identifying one or more dynamic models based on the requirements and the respective types of one or more transport system digital twins.

[0094] In the embodiment, one or more dynamic models are identified using a lookup table.

[0095] In this embodiment, the digital twin dynamic model system acquires data from a selected data source via a digital twin I / O system.

[0096] According to some embodiments of the present disclosure, a method is disclosed that includes: receiving import data from one or more data sources, the import data corresponding to a transportation system; generating a transportation system digital twin representing the transportation system based on the import data; identifying one or more transportation entities within the transportation system; generating a set of discrete digital twins representing one or more transportation entities within the transportation system; embedding the set of discrete digital twins into 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 of the sensor system via the connection; and updating at least one of the transportation system digital twin and the set of discrete digital twins based on the real-time sensor data.

[0097] In this embodiment, the connection to the sensor system is established via an application programming interface (API).

[0098] In some embodiments, a set of transport system digital twins and discrete digital twins is a visual digital twin configured to be rendered in a visual manner. In some embodiments, the method further includes outputting the visual digital twin to a client application that displays the visual digital twin via a virtual reality headset. In some embodiments, the method further includes outputting the visual digital twin to a client application that displays the visual digital twin via a display device of a user device. In some embodiments, the method further includes outputting the visual digital twin to a client application that displays the visual digital twin within a display interface, overlaying information related to the visual digital twin onto the visual digital twin or displaying it within the display interface. In some embodiments, the method further includes outputting the visual digital twin to a client application that displays the visual digital twin via an augmented reality-enabled device.

[0099] In some embodiments, the method further includes instantiating a graph database having a set of nodes connected by edges, where a first node of the set of nodes contains data defining a transport system digital twin, and one or more entity nodes each contain data defining each discrete digital twin of a set of discrete digital twins. In some embodiments, each edge represents a relationship between the two respective digital twins. In some of these embodiments, embedding discrete digital twins involves connecting entity nodes corresponding to each discrete digital twin to the first node with edges representing the respective relationships between the transport entities represented by each discrete digital twin and the transport system. In some embodiments, each edge represents a spatial relationship between the two respective digital twins. In some embodiments, each edge represents an operational relationship between the two respective digital twins. In some embodiments, each edge stores metadata corresponding to the relationship between the two respective digital twins. In some embodiments, each entity node of one or more entity nodes contains one or more properties of the respective properties of the respective transport entity represented by the entity node. In some embodiments, each entity node of one or more entity nodes contains one or more behaviors of the respective properties of the respective transport entity represented by the entity node. In some embodiments, a transport system node includes one or more properties of the transport system. In some embodiments, a transport system node includes one or more behaviors of the transport system.

[0100] In some embodiments, the method further includes performing simulations based on a set of transport system digital twins and discrete digital twins. In some embodiments, the simulation simulates the operation of a machine that produces an output based on a set of inputs. In some embodiments, the simulation simulates the vibration patterns of bearings in a machine of a transport system.

[0101] In the embodiment, one or more transport entities are selected from a set of mechanical parts, infrastructure parts, equipment parts, workpiece parts, tool parts, container parts, vehicle parts, chassis parts, drivetrain parts, electrical parts, fluid handling parts, mechanical parts, power parts, manufacturing parts, energy production parts, material extraction parts, workers, robots, assembly lines, and vehicles.

[0102] In the embodiment, the transport system includes one of the following: a mobile factory, a mobile energy production facility, a mobile material extraction facility, a mining vehicle or equipment, a drilling / tunneling vehicle or equipment, a mobile food processing facility, a cargo ship, a tanker ship, and a mobile storage facility.

[0103] In this embodiment, the imported data includes a three-dimensional scan of the transport system.

[0104] In this embodiment, the imported data includes a LiDAR scan of the transport system.

[0105] In one embodiment, generating a digital twin of a transport system includes generating a set of surfaces of the transport system.

[0106] In one embodiment, generating a digital twin of a transport system involves constructing a set of dimensions for the transport system.

[0107] In one embodiment, generating a set of discrete digital twins involves importing a predefined digital twin of a transport entity from the transport entity's supplier, the predefined digital twin includes the transport entity's characteristics and behavior.

[0108] In one embodiment, generating a set of discrete digital twins includes classifying transport entities in the import data of a transport system and generating discrete digital twins corresponding to the classified transport entities.

[0109] According to aspects of the present disclosure, a system for monitoring interactions within a transport system includes a digital twin data store and one or more processors. The digital twin data store includes data collected by a set of proximity sensors placed within the transport system, the data includes location data indicating the location of each of several elements within the transport system, and the one or more processors include location data indicating the location of each of several elements within the transport system. The one or more processors are configured to maintain a transport system digital twin for the transport system via the digital twin data store, receive signals from the several elements indicating that at least one of the proximity sensors in the set of proximity sensors has been activated by a real-world element, and in response to the activation of the set of proximity sensors, collect updated location data of the real-world element using the set of proximity sensors and update the transport system digital twin in the digital twin data store to include the updated location data.

[0110] In this embodiment, each of the proximity sensors is configured to detect a device associated with the user.

[0111] In this embodiment, the device is a wearable device.

[0112] In this embodiment, the device is an RFID device.

[0113] In this embodiment, each element of the plurality of elements is a moving element.

[0114] In this embodiment, each element of the multiple elements is a worker.

[0115] In the embodiment, the multiple elements include mobile device elements and workers, where mobile device-position data is determined using data transmitted by each mobile device element, and worker-position data is determined using data acquired by the system.

[0116] In this embodiment, worker-location data is determined using information transmitted from a device associated with each worker.

[0117] In this embodiment, the activation of a set of proximity sensors occurs in response to the interaction between each worker and the set of proximity sensors.

[0118] In this embodiment, the activation of the proximity sensor set occurs in response to an interaction between the worker and each of the at least one proximity sensor digital twins corresponding to the proximity sensor set.

[0119] In one embodiment, one or more processors use a set of proximity sensors to collect updated positional data for multiple elements in response to the activation of a set of proximity sensors.

[0120] According to aspects of the present disclosure, a system for monitoring a transport system in which real-world elements are located includes a digital twin datastore and one or more processors. The digital twin datastore includes a set of states stored therein, the set of states includes one or more states of real-world elements, each state in the set of states is uniquely identifiable by a set of identification criteria from a set of monitored attributes, the set of monitored attributes corresponds to signals received from a sensor array operably coupled to the real-world elements. The one or more processors are configured to maintain a transport system digital twin for the transport system via the digital twin datastore, receive signals for one or more attributes in the set of monitored attributes via the sensor array, determine one or more current states of real-world elements in response to determining that the signals for one or more attributes satisfy the respective set of identification criteria, and update the transport system digital twin to include one or more current states of real-world elements in response to determining the current states. The current states correspond to each state in the set of states.

[0121] In one embodiment, the cognitive intelligence system stores identification criteria in a digital twin data store.

[0122] In one embodiment, the cognitive intelligence system, in response to receiving an identification criterion, updates the trigger conditions for the set of attributes being monitored to include the updated trigger conditions.

[0123] In this embodiment, the updated trigger condition is to reduce the time interval between receiving a sensed attribute from a set of monitored attributes.

[0124] In the embodiment, the sensed attribute is one or more attributes that satisfy each set of identification criteria.

[0125] In this embodiment, the perceived attributes are all the attributes corresponding to each real-world element.

[0126] In one embodiment, the cognitive intelligence system determines whether instructions exist to respond to a state, and in response to determining that no instructions exist, the cognitive intelligence system uses a digital twin simulation system to determine instructions to respond to the state.

[0127] In the embodiment, the digital twin simulation system and the cognitive intelligence system repeatedly iterate through the simulation values ​​and response actions until the associated cost function is minimized, and one or more processors are further configured to store the response actions that minimize the associated cost function in the digital twin data store in response to the minimization of the associated cost function.

[0128] In this embodiment, the cognitive intelligence system is configured to influence state-related response behaviors.

[0129] In one embodiment, the cognitive intelligence system is configured to stop the operation of one or more real-world elements identified by the response action.

[0130] In one embodiment, the cognitive intelligence system is configured to determine resources for a transport system identified by a response behavior and to modify those resources in response.

[0131] In this embodiment, the resource includes data transfer bandwidth, and modifying the resource includes establishing additional connections in order to increase the data transfer bandwidth.

[0132] According to aspects of the present disclosure, a system for monitoring navigation route data via a transport system in which real-world elements are arranged includes a digital twin datastore and one or more processors. The digital twin datastore includes a transport system digital twin corresponding to the transport system and worker digital twins corresponding to each worker in a set of workers in the transport system. One or more processors are configured to maintain the transport system digital twin via the digital twin datastore to include the simultaneous positions of the set of workers in the transport system, monitor the movement of each worker in the set of workers via a sensor array, determine the navigation route data for each worker in response to detecting the movement of each worker, update the transport system digital twin to include a display of the navigation route data for each worker, and move the worker digital twin along the route of the navigation route data.

[0133] In an embodiment, one or more processors are further configured to update navigation route data for the remaining workers in a set of workers in response to representing the movement of each worker.

[0134] In one embodiment, the navigation route data includes a route for collecting vibration measurements from one or more machines within the transport system.

[0135] In this embodiment, navigation route data is used that is automatically transmitted to the system from one or more personal devices.

[0136] In this embodiment, the personal device is a mobile device having cellular data capabilities.

[0137] In this embodiment, the personal device is a wearable device related to the worker.

[0138] In this embodiment, navigation route data is determined via environment-related sensors.

[0139] In this embodiment, the navigation route data is determined using historical navigation data stored in a digital twin data store.

[0140] In this embodiment, historical route data was obtained using each worker.

[0141] In this embodiment, past route data was obtained using a different worker.

[0142] In this embodiment, the historical route data is associated with the worker's current task.

[0143] In this embodiment, the digital twin data store includes a transport system digital twin.

[0144] In an embodiment, one or more processors are further configured to determine the presence of a conflict between navigation route data and the transport system digital twin, modify the navigation route data for the worker in response to determining the accuracy of the transport system digital twin via a sensor array, and update the transport system digital twin to resolve the conflict in response to determining the inaccuracy of the transport system digital twin via a sensor array.

[0145] In this embodiment, the transport system digital twin is updated using collected data transmitted from the workers.

[0146] In the embodiment, the collected data includes proximity sensor data, image data, or a combination thereof.

[0147] According to aspects of this disclosure, a system for monitoring navigation route data includes a digital twin datastore and one or more processors. The digital twin datastore stores a transport system digital twin embedded with real-world element digital twins, the transport system digital twin providing a digital twin of the transport system, each real-world element digital twin providing other digital twins of corresponding real-world elements in the transport system, the corresponding real-world elements including a set of workers. One or more processors are configured to monitor the movement of each worker in the set of workers, determine navigation route data for at least one worker in the set of workers, and use the navigation route data to represent the movement of at least one worker by the movement of the associated digital twin.

[0148] In one embodiment, one or more processors are configured to further update, in response to representing the movement of at least one worker, to determine navigation route data for the remaining workers in the set of workers.

[0149] In one embodiment, the navigation route data includes a route for collecting vibration measurements from one or more machines within the transport system.

[0150] In this embodiment, navigation route data is used that is automatically transmitted to the system from one or more personal devices.

[0151] In this embodiment, the personal device is a mobile device having cellular data capabilities.

[0152] In this embodiment, the personal device is a wearable device related to the worker.

[0153] In this embodiment, navigation route data is determined via environment-related sensors.

[0154] In this embodiment, the navigation route data is determined using historical navigation data stored in a digital twin data store.

[0155] In this embodiment, past route data is acquired using each worker.

[0156] In this embodiment, past route data is acquired using a different worker.

[0157] In this embodiment, the historical route data is associated with the worker's current task.

[0158] In this embodiment, the digital twin data store includes a transport system digital twin.

[0159] In an embodiment, one or more processors are further configured to determine the presence of a conflict between navigation route data and the transport system digital twin, modify the navigation route data for the worker in response to determining the accuracy of the transport system digital twin via a sensor array, and update the transport system digital twin to resolve the conflict in response to determining the inaccuracy of the transport system digital twin via a sensor array.

[0160] In this embodiment, the transport system digital twin is updated using collected data transmitted from the workers.

[0161] In the embodiment, the collected data includes proximity sensor data, image data, or a combination thereof.

[0162] According to aspects of the present disclosure, a system for representing a workpiece object in a digital twin includes a digital twin datastore and one or more processors. The digital twin datastore stores a transport system digital twin in which digital twins of real-world elements are embedded, the transport system digital twin provides a digital twin of the transport system, each real-world element digital twin provides other digital twins of corresponding real-world elements in the transport system, the corresponding real-world elements include workpieces and workers. One or more processors are configured to use a digital twin simulation system to simulate a set of physical interactions performed on a workpiece by a worker, the simulation including obtaining a set of physical interactions performed on a workpiece by a worker, determining an expected duration for each physical interaction in the set of physical interactions based on the worker's historical data, and storing in the digital twin datastore a digital twin of the workpiece corresponding to the execution of the set of physical interactions on the workpiece.

[0163] In this embodiment, historical data is obtained from user input data.

[0164] In this embodiment, historical data is obtained from a sensor array within the transport system.

[0165] In this embodiment, historical data is acquired from a wearable device worn by the worker.

[0166] In this embodiment, each data point in the historical data includes an index for a first time and a second time, where the first time is the time of the physical interaction.

[0167] In one embodiment, the second time is the time to begin the worker's expected rest period.

[0168] In the embodiment, the historical data further includes an indicator of duration relative to the expected rest time.

[0169] In this embodiment, the second time is the time to end the worker's expected rest period.

[0170] In the embodiment, the historical data further includes an indicator of duration relative to the expected rest time.

[0171] In this embodiment, the second time is the time to end the worker's unexpected break.

[0172] In one embodiment, the historical data further includes an indicator of the duration of unexpected breaks.

[0173] In one embodiment, each datum of the historical data includes an index of a series of interactions between the worker and several other workpieces prior to performing a set of physical interactions with the workpiece.

[0174] In this embodiment, each piece of historical data includes an indicator of the consecutive days that the worker was present in the transport system.

[0175] In this embodiment, each piece of historical data includes an indicator of the worker's age.

[0176] In embodiments, the historical data further includes indices for a first duration of the worker's expected rest period and a second duration of the worker's unexpected rest period, each data point in the historical data including an index of multiple times, an index of the worker's consecutive interactions with multiple other workpieces prior to a series of physical interactions with the workpiece, and an index of the number of consecutive days the worker was in the transport system, or an index indicating the worker's age. The multiple times include a first time, a second time, a third time, and a fourth time. The first time is the duration of the physical interaction, the second time is the start time of the expected rest period, the third time is the end time of the expected rest period, and the fourth time is the end time of the unexpected rest period.

[0177] In one embodiment, the workpiece digital twin includes a first workpiece digital twin corresponding to the workpiece before the execution of a physical interaction, and a second workpiece digital twin corresponding to the workpiece after the execution of a series of physical interactions.

[0178] In one embodiment, the workpiece digital twin is a plurality of workpiece digital twins, each of which corresponds to a workpiece after performing one of a set of physical interactions.

[0179] According to aspects of this disclosure, a system for inducing an experience via a wearable device includes a digital twin datastore and one or more processors. The digital twin datastore stores a transport system digital twin in which digital twins of real-world elements are embedded, the transport system digital twin provides a digital twin of the transport system, each real-world element digital twin provides other digital twins of corresponding real-world elements within the transport system, and the corresponding real-world elements include a wearable device worn by a wearer within the transport system. One or more processors embed a set of control instructions for the wearable device within the digital twin and are configured to induce an experience for the wearer of the wearable device in response to the interaction between the wearable device and one of the digital twins.

[0180] In the embodiment, the wearable device is configured to output video, audio, haptic feedback, or a combination thereof to evoke an experience for the wearer.

[0181] In this embodiment, the experience is a virtual reality experience.

[0182] In one embodiment, the wearable device includes an image capture device, and the interaction includes the wearable device capturing an image of a digital twin.

[0183] In the embodiment, the wearable device includes a display device, and the experience includes the display of information related to each digital twin.

[0184] In this embodiment, the displayed information includes financial data related to the digital twin.

[0185] In the embodiment, the information displayed includes profits or losses related to the operation of the digital twin.

[0186] In the embodiment, the displayed information includes information relating to an obstruction element that is at least partially blocked by a foreground element.

[0187] In this embodiment, the displayed information includes the operating parameters of the blocking element.

[0188] In the embodiment, the displayed information further includes a comparison with the design parameters corresponding to the displayed operating parameters.

[0189] In the embodiment, the comparison includes changing the display of the operation parameter to change the color, size, or display duration of the operation parameter.

[0190] In the embodiment, the information includes a virtual model of the occluded element, which is overlaid on the occluded element and visualized together with the foreground element.

[0191] In the embodiment, the information includes indicators for removable elements configured to provide access to the occluding element. Each indicator is displayed in close proximity to its respective removable element.

[0192] In this embodiment, the indicators are displayed sequentially, with a first indicator corresponding to a first removable element being displayed, and a second indicator corresponding to a second removable element being displayed in response to a worker removing the first removable element.

[0193] According to aspects of this disclosure, a system for embedding device outputs into a transport system digital twin includes a digital twin datastore and one or more processors. The digital twin datastore stores a transport system digital twin into which real-world element digital twins are embedded, the transport system digital twin provides a digital twin of the transport system, each real-world element digital twin provides other digital twins of corresponding real-world elements in the transport system, the real-world elements include co-location and mapping sensors. One or more processors are configured to acquire location information from the co-location and mapping sensors, determine that the co-location and mapping sensors are located within the transport system, collect mapping information, route information, or a combination thereof from the co-location and mapping sensors, and update the transport system digital twin using the mapping information, route information, or a combination thereof. The collection is performed in response to the determination that the co-location and mapping sensors are located within the transport system.

[0194] In the embodiment, one or more processors are further configured to detect objects in the mapping information, determine for each detected object in the mapping information whether the detected object corresponds to an existing real-world element digital twin, and, in response to determining that the detected object does not correspond to an existing real-world element digital twin, to add it. The digital twin of the detected object is added to the digital twin of the real-world element in the digital twin datastore using a digital twin management system, and, in response to determining that the detected object corresponds to an existing digital twin of the real-world element, the digital twin of the real-world element is updated to include the new information detected by the simultaneous position and mapping sensors.

[0195] In this embodiment, the simultaneous position and mapping sensors are configured to generate mapping information using a suboptimal mapping algorithm.

[0196] In the embodiment, the suboptimal mapping algorithm generates boundary region representations for elements within the transport system.

[0197] In one embodiment, one or more processors are further configured to acquire objects detected by a suboptimal mapping algorithm, determine whether the detected objects correspond to an existing real-world element digital twin, and, in response to determining that the detected objects correspond to an existing real-world element digital twin, update the mapping information to include dimensional information of the real-world element digital twin.

[0198] In this embodiment, the updated mapping information is provided to the simultaneous position and mapping sensors in order to optimize navigation through the transport system.

[0199] In the embodiment, one or more processors are further configured to request updated data for the detected object from a simultaneous position and mapping sensor configured to generate a refined map of the detected object, in response to determining that the detected object does not correspond to an existing real-world element digital twin.

[0200] In the embodiment, the simultaneous position and mapping sensors provide updated data using a second algorithm. The second algorithm is configured to increase the resolution of the detected objects.

[0201] In the embodiment, the simultaneous position and mapping sensors capture updated data about the real-world elements corresponding to the detected object in response to receiving a request.

[0202] In this embodiment, the simultaneous position and mapping sensors are located within an autonomous vehicle that navigates the transportation system.

[0203] In this embodiment, the navigation of the autonomous vehicle includes the use of a digital twin received from a digital twin data store.

[0204] According to aspects of the present disclosure, a system for embedding device outputs into a transport system digital twin includes a digital twin datastore and one or more processors. The digital twin datastore stores a transport system digital twin having real-world element digital twins embedded therein. The transport system digital twin provides a digital twin of the transport system. Each real-world element digital twin provides other digital twins for corresponding real-world elements in the transport system. The real-world elements include light-detecting and ranging sensors. The one or more processors are configured to acquire outputs from the light-detecting and ranging sensors and embed the outputs of the light-detecting and ranging sensors into the transport system digital twin to define at least one external feature of a real-world element in the transport system.

[0205] In the embodiment, one or more processors are further configured to analyze the output to determine a plurality of detected objects in the output of the light detection and distance measuring sensors. Each of the plurality of detected objects has a closed shape.

[0206] In the embodiment, one or more processors are further configured to compare a plurality of detected objects with real-world element digital twins in a digital twin data store, and to update each of the plurality of detected objects in response to determining that the detected object corresponds to one or more of the real-world element digital twins. They are also characterized by updating each real-world element digital twin in the digital twin data store and adding a new real-world element digital twin in response to determining that the detected object does not correspond to a real-world element digital twin.

[0207] In this embodiment, the output from the optical detection and ranging sensors is received at a first resolution, and one or more processors are further configured to compare a plurality of detected objects with a real-world element digital twin in a digital twin data store, and for each of the plurality of detected objects that do not correspond to a real-world element digital twin, to increase the scan resolution to a second resolution and to instruct the optical detection and ranging sensors to scan the detected object using the second resolution.

[0208] In this embodiment, the scan has a resolution at least five times that of the first resolution.

[0209] In this embodiment, the scan resolution is at least 10 times that of the first resolution.

[0210] In the embodiment, the output from the optical detection and ranging sensors is received at a first resolution, and one or more processors are further configured to compare a plurality of detected objects with real-world element digital twins in the digital twin datastore, and for each of the plurality of detected objects, update the respective real-world element digital twin in the digital twin datastore in response to determining that the detected object corresponds to one or more real-world element digital twins. In response to determining that the detected object does not correspond to a real-world element digital twin, the system is further configured to instruct the optical detection and ranging sensors to increase the scan resolution to a second resolution, perform a scan of the detected object using the second resolution, and add a new real-world element digital twin for the detected object to the digital twin datastore.

[0211] According to aspects of this disclosure, a system for embedding device outputs into a transport system digital twin includes a digital twin datastore and one or more processors. The digital twin datastore includes a transport entity system digital twin that provides a digital twin of a transport entity. The transport system includes real-world elements placed within it. The real-world elements include a plurality of wearable devices. The transport system digital twin includes a plurality of real-world element digital twins embedded within it. Each real-world element digital twin corresponds to at least one of each of the plurality of wearable devices. One or more processors are configured to take outputs from each of the plurality of wearable devices and, in response to detecting trigger conditions, update the transport system digital twin with the outputs from the wearable devices.

[0212] In this embodiment, the trigger condition is the reception of output from a wearable device.

[0213] In this embodiment, the trigger condition is that it is determined that the output from the wearable device is different from a previously stored output from the wearable device.

[0214] In this embodiment, the trigger condition is the determination that the output received from one wearable device among multiple wearable devices is different from a previously stored output from another wearable device.

[0215] In one embodiment, the trigger condition includes a mismatch between an output from one wearable device and a simultaneous output from another wearable device.

[0216] In the embodiment, the trigger condition includes a mismatch between the output from the wearable device and the simulated value of the wearable device.

[0217] In this embodiment, the trigger condition includes user interaction with a digital twin corresponding to a wearable device.

[0218] In embodiments, one or more processors are further configured to detect objects in mapping information received from simultaneous position and mapping sensors. For each object detected in the mapping information, the system determines whether the detected object corresponds to an existing real-world element digital twin, and in response to determining that the detected object does not correspond to an existing real-world element digital twin, it is further configured to: add a digital twin of the detected object to the real-world element digital twin in the digital twin datastore using a digital twin management system, and in response to determining that the detected object corresponds to an existing real-world element digital twin, update the real-world element digital twin to include the new information detected by the simultaneous position and mapping sensors.

[0219] In this embodiment, the simultaneous position mapping sensor is configured to generate mapping information using a suboptimal mapping algorithm.

[0220] In the embodiment, the suboptimal mapping algorithm generates boundary region representations for elements within the transport system.

[0221] In one embodiment, one or more processors are further configured to acquire objects detected by a suboptimal mapping algorithm, determine whether the detected objects correspond to an existing real-world element digital twin, and update the mapping information to include dimensional information from the real-world element digital twin in response to determining that the detected objects correspond to an existing real-world element digital twin.

[0222] In this embodiment, the updated mapping information is provided to the simultaneous position and mapping sensors in order to optimize navigation through the transport system.

[0223] In the embodiment, one or more processors are further configured to request updated data for the detected object from a simultaneous position and mapping sensor configured to generate a refined map of the detected object, in response to determining that the detected object does not correspond to an existing real-world element digital twin.

[0224] In the embodiment, the simultaneous position and mapping sensors provide updated data using a second algorithm. The second algorithm is configured to increase the resolution of the detected objects.

[0225] In one embodiment, the simultaneous position mapping sensor captures updated data about the real-world elements corresponding to the detected object in response to receiving a request.

[0226] In this embodiment, the simultaneous position and mapping sensors are located within an autonomous vehicle that navigates the transportation system.

[0227] In one embodiment, the navigation of the autonomous vehicle includes the use of a digital twin of real-world elements received from a digital twin data store.

[0228] According to aspects of the present disclosure, a system for representing attributes in a digital twin of a transport system includes a digital twin datastore and one or more processors. The digital twin datastore stores a transport system digital twin, which includes real-world element digital twins, the transport system digital twin corresponds to a transport system, each real-world element digital twin provides a digital twin of each real-world element located within the transport system, the real-world element digital twin includes a mobile element digital twin, each mobile element digital twin provides a digital twin of each mobile element within the real-world element. One or more processors are configured to determine the location of each mobile element in response to the occurrence of a trigger condition, and to update the mobile element digital twin corresponding to the mobile element in response to determining the location of the mobile element to reflect the location of the mobile element.

[0229] In this embodiment, the moving element is a worker in the transport system.

[0230] In this embodiment, the moving element is a vehicle within the transport system.

[0231] In this embodiment, the trigger condition is the expiration of a dynamically determined time interval.

[0232] In one embodiment, the dynamically determined time interval is increased in response to determining a single moving element within the transport system.

[0233] In the embodiment, the dynamically determined time interval is increased in response to determining the occurrence of a predetermined period of reduced environmental activity.

[0234] In one embodiment, the dynamically determined time interval is reduced in response to determining abnormal activity within the transport system.

[0235] In this embodiment, the dynamically determined time interval is a first time interval, and the dynamically determined time interval is reduced to a second time interval in response to determining the movement of the moving element.

[0236] In this embodiment, the dynamically determined time interval is increased from the second time interval to the first time interval in response to determining the non-movement of the moving element for at least a third time interval.

[0237] In one embodiment, the trigger condition is the expiration of a time interval. The time interval is calculated based on the probability that the moving element has moved.

[0238] In this embodiment, the trigger condition is the proximity of one moving element to another moving element.

[0239] In this embodiment, the trigger condition is based on the density of moving elements within the transport system.

[0240] In this embodiment, route information is obtained from the navigation module of the moving element.

[0241] In an embodiment, one or more processors are further configured to obtain route information, which includes using multiple sensors in the transport system to detect the movement of a moving element, to obtain the destination of the moving element, to use multiple sensors in the transport system to calculate an optimal route for the moving element, and to instruct the moving element to navigate the optimal route.

[0242] In one embodiment, the optimized route includes using route information from other movement elements within the real-world element.

[0243] In the embodiment, the optimized route minimizes interaction between mobile elements and humans within the transport system.

[0244] In this embodiment, the mobile entity includes autonomous vehicles and non-autonomous vehicles, and the optimized route reduces interaction between the autonomous and non-autonomous vehicles.

[0245] In the embodiments, traffic modeling includes the use of particle traffic models, trigger response moving element tracking traffic models, macroscopic traffic models, microscopic traffic models, mesoscopic traffic models, or combinations thereof.

[0246] According to aspects of this disclosure, a system for representing design specification information includes a digital twin datastore and one or more processors. The digital twin datastore stores a transport system digital twin, which includes real-world element digital twins, the transport system digital twins correspond to a transport system, and each real-world element digital twin provides a digital twin of each real-world element located within the transport system. One or more processors are configured to determine the design specifications of each real-world element, associate the design specifications with the real-world element digital twin, and display the design specifications to a user in response to a user interacting with the real-world element digital twin.

[0247] In one embodiment, a user interacting with a real-world element digital twin includes the user selecting the real-world element digital twin.

[0248] In this embodiment, a user interacting with a real-world element digital twin includes the user pointing an image capture device towards the real-world element digital twin.

[0249] In this embodiment, the imaging device is a wearable device.

[0250] In this embodiment, the real-world element digital twin is a transportation system digital twin.

[0251] In this embodiment, the design specifications are stored in a digital twin data store in response to user input.

[0252] In this embodiment, the design specifications are determined using a digital twin simulation system.

[0253] In the embodiment, one or more processors are further configured to detect one or more simultaneous operating parameters for each real-world element using sensors in the transport system, compare the one or more simultaneous operating parameters with design specifications, and automatically display the design specifications, the one or more simultaneous operating parameters, or a combination thereof in response to any mismatch between the one or more simultaneous operating parameters and the design specifications. The one or more simultaneous operating parameters correspond to the design specifications of the real-world element.

[0254] In this embodiment, the display of design specifications includes the display of operating parameters at the same time.

[0255] In the embodiment, the display of the design specifications includes a display of the source of the specification information.

[0256] In an embodiment, the source display informs the user that the design specifications were determined through the use of a digital twin simulation system. A more complete understanding of this disclosure will be derived from the following description and accompanying drawings and the claims.

[0257]

[0258] According to aspects of this disclosure, a method for configuring a role-based digital twin is provided. The method includes: a step of receiving an organizational definition of an enterprise by a processing system having one or more processors, wherein the organizational definition defines a set of roles within the enterprise; a step of generating an organizational digital twin of the enterprise by the processing system based on the organizational definition, wherein the organizational digital twin is a digital representation of the enterprise's organizational structure; a step of determining a set of relationships between different roles within the set of roles by the processing system based on the organizational definition; a step of determining a set of settings for a role from the set of roles based on the determined set of relationships; a step of linking the identification information of each individual to the role; a step of determining the configuration of a presentation layer of a role-based digital twin corresponding to a role by the processing system based on the role settings linked to the identification information, wherein the presentation layer configuration defines a set of states to be depicted in the role-based digital twin associated with the role; a step of determining a set of data sources that provide data corresponding to the set of states, wherein each data source provides one or more of each type of data; and a step of configuring one or more data structures received from one or more data sources, wherein one or more data structures are configured to provide data used to input one of one or more states in the role-based digital twin.

[0259] In this embodiment, the organizational definition may further identify a set of the company's physical assets.

[0260] In an embodiment, determining a set of relationships may include analyzing organizational definitions to identify the reporting structure of a company and one or more business units.

[0261] In the embodiment, the set of relationships can be inferred from the reporting structure and business units.

[0262] In this embodiment, a set of identification information may be linked to a set of roles, and each piece of identification information corresponds to a specific role within the set of roles.

[0263] In some embodiments, the organizational structure may include hierarchical components that can be embodied in a graph data structure.

[0264] In this embodiment, the set of settings for a set of roles may include role-based preference settings.

[0265] In this embodiment, role-based preference settings may be configured based on a set of role-specific templates.

[0266] In an embodiment, the set of templates may include at least one of the following: CEO template, COO template, CFO template, legal counsel template, board member template, CTO template, chief marketing officer template, information technology manager template, chief information officer template, chief data officer template, investor template, customer template, vendor template, supplier template, engineering manager template, project manager template, operations manager template, sales manager template, salesperson template, service manager template, maintenance operator template, and business development template.

[0267] In the embodiment, the settings for a set of roles may include role-based classification settings.

[0268] In an embodiment, the taxonomy setting may specify the classification used to characterize the data presented in the role-based digital twin, such that the data is presented in a classification linked to the roles corresponding to the role-based digital twin.

[0269] In one embodiment, the set of classifications includes at least one of the following: CEO classification, COO classification, CFO classification, advisor classification, director classification, CTO classification, chief marketing officer classification, information technology manager classification, chief information officer classification, chief data officer classification, investor classification, customer classification, vendor classification, supplier classification, engineering manager classification, project manager classification, operations manager classification, sales manager classification, salesman classification, service manager classification, maintenance operator classification, and business development classification.

[0270] In an embodiment, at least one role in the set of roles may be selected from the roles of CEO, COO, CFO, attorney, director, CTO, information technology manager, chief information officer, chief data officer, human resources manager, investor, engineering manager, accountant, auditor; resource planning, public relations manager, project manager, operations manager, research and development, engineer (including but not limited to mechanical engineers, electrical engineers, semiconductor engineers, chemical engineers, computer science engineers, data science engineers, network engineers, or other types of engineers); and business development.

[0271] In an embodiment, at least one role may be selected from the roles of factory manager, factory operations, factory worker, power plant manager, power plant operations, power plant worker, equipment service, and equipment maintenance worker.

[0272] In one embodiment, at least one role may be selected from the following roles: Chief Marketing Officer, Product Development, Supply Chain Manager, Product Design, Marketing Analyst, Product Manager, Competitive Analyst, Customer Service Representative, Procurement Operator, Inbound Logistics Operator, Customer, Supplier, Vendor, Demand Management, Marketing Manager, Sales Manager, Service Manager, Demand Forecaster, Warehouse Manager, Salesperson, and Distribution Center Manager.

[0273] A part of the present disclosure provides a method for constructing a workforce digital twin. The method includes the steps of: representing the organizational structure of an enterprise in the enterprise's digital twin; analyzing the organizational structure to infer relationships between sets of roles within the organizational structure; defining the enterprise's workforce based on the relationships and roles; and configuring the presentation layer of the digital twin to represent the enterprise as a set of workforce having the aforementioned sets of attributes and relationships.

[0274] In one embodiment, the digital twin can be integrated with an enterprise resource planning system that operates on a data structure representing a set of roles within the enterprise, so that changes in the enterprise resource planning system are automatically reflected in the digital twin.

[0275] In this embodiment, the organizational structure may include hierarchical components.

[0276] In this embodiment, hierarchical components may be embodied in a graph data structure.

[0277] In this embodiment, the worker may be a factory worker, a plant worker, a resource extraction worker, or any other type of worker.

[0278] In one embodiment, at least one workforce role may be selected from among the roles of CEO, COO, CFO, attorney, director, CTO, information technology manager, chief information officer, chief data officer, investor, engineering manager, project manager, operations manager, and business development.

[0279] In an embodiment, the digital twin may represent recommendations relating to workforce training, recommendations relating to workforce augmentation, recommendations relating to the configuration of a set of operations involving workforce, recommendations relating to workforce configuration, or any other type of recommendation.

[0280] It will be understood that any combination of features from the methods disclosed herein and / or features from the systems disclosed herein may be used together, and / or any features from any or all of these embodiments may be combined with any of the features of the embodiments and / or examples disclosed herein to achieve the advantages described herein.

[0281] In the attached figures, the same reference numerals indicate the same or functionally similar elements throughout the individual figures, which are incorporated herein by reference and form part of the following detailed description, and which help to further illustrate various embodiments and to explain all the various principles and advantages according to the systems and methods disclosed herein. [Brief explanation of the drawing]

[0282] [Figure 1] Figure 1 is a perspective view showing a structure for a transport system, illustrating specific exemplary components and arrangements related to various embodiments of the present disclosure.

[0283] [Figure 2] Figure 2 is a perspective view illustrating the use of a hybrid neural network to optimize vehicle powertrain components related to various embodiments of this disclosure.

[0284] [Figure 3] Figure 3 is an illustrative diagram showing a set of states that may be provided as input to and / or controlled by an expert system / artificial intelligence (AI) system relating to various embodiments of the present disclosure.

[0285] [Figure 4] Figure 4 is an illustrative diagram showing a range of parameters that may be taken as input by an expert system or AI system or its components as described through this disclosure, or that may be provided as output from such a system and / or one or more sensors, cameras, or external systems related to various embodiments of this disclosure.

[0286] [Figure 5] Figure 5 is a perspective view showing a set of vehicle user interfaces related to various embodiments of the present disclosure.

[0287] [Figure 6] Figure 6 is a perspective view showing a set of interfaces between transport system components related to various embodiments of the present disclosure.

[0288] [Figure 7] Figure 7 is a perspective view illustrating a data processing system capable of processing data from various sources related to various embodiments of this disclosure.

[0289] [Figure 8] Figure 8 is a perspective view showing a set of algorithms that may be performed in connection with one or more of the many embodiments of the transport system described herein in relation to various embodiments of the present disclosure.

[0290] [Figure 9] Figure 9 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0291] [Figure 10] Figure 10 is a perspective view illustrating a system described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0292] [Figure 11] Figure 11 is a perspective view illustrating a method described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0293] [Figure 12] Figure 12 is a perspective view illustrating a system described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0294] [Figure 13] Figure 13 is a perspective view illustrating a method described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0295] [Figure 14] Figure 14 is a perspective view illustrating a system described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0296] [Figure 15] Figure 15 is a perspective view illustrating a method described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0297] [Figure 16] Figure 16 is a perspective view illustrating a system described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0298] [Figure 17] Figure 17 is a perspective view illustrating a method described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0299] [Figure 18]FIG. 18 is a perspective view illustrating a system described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0300] [Figure 19] FIG. 19 is a perspective view illustrating a method described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0301] [Figure 20] FIG. 20 is a perspective view illustrating a method described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0302] [Figure 21] FIG. 21 is a perspective view illustrating a method described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0303] [Figure 22] FIG. 22 is a perspective view illustrating a system described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0304] [Figure 23] FIG. 23 is a perspective view illustrating a method described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0305] [Figure 24] FIG. 24 is a perspective view illustrating a method described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0306] [Figure 25] FIG. 25 is a perspective view illustrating a system described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0307] [Figure 26] FIG. 26 is a perspective view illustrating a method described throughout the present disclosure in connection with various embodiments of the present disclosure.

[0308] [Figure 26A] Figure 26A is a perspective view showing the system described throughout this disclosure in relation to various embodiments of this disclosure.

[0309] [Figure 27] Figure 27 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0310] [Figure 28] Figure 28 is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0311] [Figure 29] Figure 29 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0312] [Figure 30] Figure 30 is a perspective view illustrating the system described through this disclosure in relation to various embodiments of this disclosure.

[0313] [Figure 31] Figure 31 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0314] [Figure 32] Figure 32 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0315] [Figure 33] Figure 33 is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0316] [Figure 34] Figure 34 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0317] [Figure 35] Figure 35 is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0318] [Figure 36] Figure 36 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0319] [Figure 37] Figure 37 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0320] [Figure 38] Figure 38 is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0321] [Figure 39] Figure 39 is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0322] [Figure 40] Figure 40 is a perspective view illustrating the methods described throughout this disclosure relating to various embodiments of this disclosure.

[0323] [Figure 41] Figure 41 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0324] [Figure 42] Figure 42 is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0325] [Figure 43] Figure 43 is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0326] [Figure 44] Figure 44 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0327] [Figure 45] Figure 45 is a perspective view illustrating the systems and methods described through this disclosure relating to various embodiments of this disclosure.

[0328] [Figure 46] Figure 46 is a perspective view illustrating the systems and methods described through this disclosure relating to various embodiments of this disclosure.

[0329] [Figure 47] Figure 47 is a perspective view illustrating the systems and methods described through this disclosure relating to various embodiments of this disclosure.

[0330] [Figure 48] Figure 48 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0331] [Figure 49] Figure 49 is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0332] [Figure 50] Figure 50 is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0333] [Figure 51] Figure 51 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0334] [Figure 52]Figure 52 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0335] [Figure 53] Figure 53 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0336] [Figure 54] Figure 54 is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0337] [Figure 55] Figure 55 is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0338] [Figure 56] Figure 56 is a perspective view illustrating the system described through this disclosure in relation to various embodiments of this disclosure.

[0339] [Figure 57] Figure 57 is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0340] [Figure 58] Figure 58 is a perspective view illustrating a system described through this disclosure relating to various embodiments of this disclosure.

[0341] [Figure 59] Figure 59 is a perspective view showing a structure for a transport system including a digital twin system of vehicles, illustrating specific exemplary components and arrangements related to various embodiments of the present disclosure.

[0342] [Figure 60]Figure 60 is a schematic diagram of a digital twin system integrated with an identity and access management system, according to a particular embodiment of the present disclosure.

[0343] [Figure 61] Figure 61 is a schematic diagram of the interface of a digital twin system presented on a driver's user device of a vehicle relating to various embodiments of the present disclosure.

[0344] [Figure 62] Figure 62 is a schematic diagram illustrating the interaction between the driver and the digital twin using one or more views and modes of the interface according to exemplary embodiments of the present disclosure.

[0345] [Figure 63] Figure 63 is a schematic diagram of the interface of a digital twin system presented to a vehicle manufacturer's user device according to various embodiments of this disclosure.

[0346] [Figure 64] Figure 64 illustrates a scenario in which a manufacturer uses a digital twin interface quality view to run a simulation and generate what-if scenarios for vehicle quality testing, according to an exemplary embodiment of the present disclosure.

[0347] [Figure 65] Figure 65 is a schematic diagram of the interface of the digital twin system presented to the user device at the vehicle dealership.

[0348] [Figure 66] Figure 66 illustrates an exemplary embodiment of a dealer-to-digital twin interaction using one or more views, aimed at personalizing the customer experience when purchasing a vehicle.

[0349] [Figure 67]Figure 67 illustrates the service and maintenance views presented to vehicle users, including drivers, vehicle manufacturers, and dealers, according to various embodiments of the present disclosure.

[0350] [Figure 68] Figure 68 shows a method used by a digital twin to detect vehicle failures and predict any future failures, according to an exemplary embodiment.

[0351] [Figure 69] Figure 69 is a perspective view showing the structure of a vehicle having a digital twin system for predictive maintenance of the vehicle, according to an exemplary embodiment of the present disclosure.

[0352] [Figure 70] Figure 70 is a flowchart illustrating a method for generating a digital twin of a vehicle according to various embodiments of the present disclosure.

[0353] [Figure 71] Figure 71 is a perspective view showing alternative structures for a transport system, including a vehicle and a digital twin system, according to various embodiments of the present disclosure.

[0354] [Figure 72] Figure 72 shows a digital twin representing a combination of sets of states for both the vehicle and the vehicle's driver, according to a particular embodiment of the present disclosure.

[0355] [Figure 73] Figure 73 is a schematic diagram illustrating a scenario in which an integrated vehicle and driver digital twin can constitute the vehicle experience, according to an exemplary embodiment.

[0356] [Figure 74] Figure 74 is a schematic diagram illustrating some examples of information technology systems for traffic artificial intelligence utilizing digital twins, according to several embodiments of the present disclosure.

[0357] [Figure 75] Figure 75 is a schematic diagram showing an example of the structure of a digital twin system according to the embodiment of this disclosure.

[0358] [Figure 76] Figure 76 is a schematic diagram showing exemplary components of a digital twin management system according to an embodiment of the present disclosure.

[0359] [Figure 77] Figure 77 is a schematic diagram illustrating an example of a digital twin I / O system that interfaces with an environment, a digital twin system, and / or its components to provide bidirectional data transfer between coupled components, according to embodiments of the present disclosure.

[0360] [Figure 78] Figure 78 is a schematic diagram illustrating an exemplary set of identification states relating to a transport system that a digital twin system may identify and / or store for access by an intelligent system (e.g., a cognitive intelligence system) or a user of the digital twin system, according to an embodiment of the present disclosure.

[0361] [Figure 79] Figure 79 is a schematic diagram illustrating an exemplary embodiment of a method for updating a set of properties of the digital twin of this disclosure on behalf of a client application and / or one or more embedded digital twins.

[0362] [Figure 80] Figure 80 shows an exemplary embodiment of the display interface of this disclosure for rendering a digital twin of a dryer centrifuge having information related to the dryer centrifuge.

[0363] [Figure 81]Figure 81 is a schematic diagram illustrating an exemplary embodiment of a method for updating a set of vibration failure level states for mechanical components, such as bearings, in a digital twin of a machine, on behalf of a client application.

[0364] [Figure 82] Figure 82 is a schematic diagram illustrating an exemplary embodiment of a method for updating a set of vibration severity unit values ​​for mechanical components such as bearings in a digital twin of a machine, on behalf of a client application.

[0365] [Figure 83] Figure 83 is a schematic diagram illustrating an exemplary embodiment of a method for updating a set of failure probability values ​​in a digital twin of a machine part on behalf of a client application.

[0366] [Figure 84] Figure 84 is a schematic diagram illustrating an exemplary embodiment of a method for updating a set of machine downtime probability values ​​in a digital twin of a traffic system on behalf of a client application.

[0367] [Figure 85] Figure 85 is a schematic diagram illustrating an exemplary embodiment of a method for updating the shutdown probability values ​​of one or more transportation systems in a digital twin of one or more transportation systems.

[0368] [Figure 86] Figure 86 is a schematic diagram illustrating an exemplary embodiment of a method for updating the cost set of machine downtime values ​​in a digital twin of a transport system.

[0369] [Figure 87] Figure 87 is a schematic diagram illustrating an exemplary 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.

[0370] [Figure 88] Figure 88 is a schematic diagram illustrating one embodiment of the method of the present disclosure.

[0371] [Figure 89] Figure 89 is a schematic diagram illustrating different types of enterprise digital twins, including executive digital twins, related to the data layer, processing layer, and application layer of an enterprise digital twin framework according to several embodiments of the present disclosure.

[0372] [Figure 90] Figure 90 is a schematic diagram illustrating an example of a method for configuring a role-based digital twin according to some embodiments of the present disclosure.

[0373] [Figure 91] Figure 91 is a schematic diagram illustrating an example of a method for configuring a digital twin of a workforce according to some embodiments of the present disclosure. [Modes for carrying out the invention]

[0374] Those skilled in the art will understand that the elements in the figures are illustrated for simplification and clarity and are not necessarily drawn to scale. For example, the dimensions of some elements in the figures may be exaggerated relative to others to help improve the understanding of many embodiments of the systems and methods disclosed herein.

[0375] Next, the present disclosure will be described in detail by referring to the accompanying drawings and exhibits to illustrate various exemplary, non-limiting embodiments thereof. However, the present disclosure may be embodied in many different forms and should not be construed as being limited to the exemplary embodiments described herein. Rather, the embodiments are provided to ensure that this disclosure is thorough and fully conveys the concepts of the present disclosure to those skilled in the art. To determine the true scope of the present disclosure, one should refer to the claims.

[0376] Before describing in detail embodiments of the systems and methods disclosed herein, it should be noted that embodiments primarily exist in combinations of methods and / or system components. Therefore, system components and methods are appropriately represented in the drawings by conventional symbols, showing only specific details appropriate for understanding the embodiments of the systems and methods disclosed herein.

[0377] All documents included in this book are incorporated into this book in their entirety by reference. References to singular items should be understood to include plural items unless explicitly stated otherwise or evident from the context, and vice versa. Grammatical conjunctions are intended to express any and all connecting combinations of joined clauses, sentences, words, etc., unless otherwise specified or evident from the context. Thus, the term “or” should generally be understood to mean “and / or,” etc., unless otherwise clearly evident from the context.

[0378] The ranges of values ​​in this specification are not intended to be limiting, but rather, unless otherwise specifically indicated herein, any and all values ​​falling within the range are to be referred to individually, and each individual value within such a range is incorporated herein as if it were separately stated herein. Words such as “about,” “approximately,” etc., when associated with numbers, are to be interpreted as indicating a deviation that would be understood by those skilled in the art to function satisfactorily for the intended purpose. Numerical ranges and / or numbers are provided herein as examples only and do not limit the scope of the embodiments described herein. Any and all examples or use of exemplary language (such as “for example,” “evidently,” etc.) provided herein are solely for the purpose of better illuminating the embodiments and do not constitute a limitation on the scope of the embodiments or claims. No wording herein should be interpreted as indicating an element not claimed that is essential to the implementation of an embodiment.

[0379] In the following explanation, terms such as “first,” “second,” “third,” “above,” and “below” are for convenience only and should not be interpreted as meaning chronological order or restricting any corresponding element unless explicitly stated otherwise. The term “set” should be understood to encompass a single component or a set having multiple components.

[0380] Referring to Figure 1, a structure for the transport system 111 is depicted, showing specific exemplary components and arrangements related to the particular embodiment described herein. The transport system 111 may include one or more vehicles 110 including 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 drivetrain, a transmission 119, and a gear set. The vehicles may have a vehicle user interface 123, which may include a set of interfaces including a steering system, buttons, levers, a touchscreen interface, an audio interface, etc., as described throughout this disclosure. The vehicles may have a set of sensors 125 (including a camera 127) for purposes such as providing input to expert system / artificial intelligence functions described throughout this disclosure, such as one or more neural networks (which may include a hybrid neural network 147 as described herein). Sensor 125 and / or external information may also be communicated to the expert system / artificial intelligence (AI) system 136 to indicate or track one or more vehicle states 144, such as the vehicle operating state 345 (Figure 3), user experience state 346 (Figure 3), and others, as described herein, which may be taken as inputs to or outputs from a set of expert system / AI components. Routing information 143 can be notified of and taken up inputs from the expert system / AI system 136, including the use of in-vehicle navigation functions and external navigation functions, such as GPS (Global Position System), triangulation routing (e.g., cell tower), and peer-to-peer routing with other vehicles 121. The collaboration engine 129 may facilitate collaboration between vehicles and / or between vehicle users for purposes such as managing collective experience and managing a fleet.Vehicles 110 may be networked with each other in a peer-to-peer manner, such as by using cognitive radio, cellular, wireless, or other networking capabilities. The AI ​​system 136 or other expert system may receive a wide range of vehicle parameters 130 as input, such as from an in-vehicle diagnostic system, telemetry system, and other software systems, as well as from sensors 125 located in the vehicle, and from external systems. In embodiments, the system may manage a set of feedback / rewards 148, incentives, etc., to induce specific user behavior, such as learning about a set of outcomes for achieving a given task or objective, and / or to provide feedback to the AI ​​system 136. The expert system or AI system 136 may notify, use, manage, or extract outputs from a set of algorithms 149, including a wide variety of those described herein. In the embodiment of the disclosure depicted in Figure 1, a data processing system 162 is connected to a hybrid neural network 147. The data processing system 162 may process data from various sources (see Figure 7). In the embodiment of the disclosure depicted in Figure 1, a system user interface 163 is connected to the hybrid neural network 147. For further disclosures regarding the interface, please refer to the following disclosures related to Figure 6. Figure 1 shows that the vehicle perimeter 164 may be part of the traffic system 111. The vehicle perimeter may include roads, weather conditions, lighting conditions, etc. Figure 1 also shows that devices 165, such as mobile phones and computer systems, navigation systems, etc., may be connected to various elements of the traffic system 111 and therefore may be part of the traffic system 111 of this disclosure.

[0381] Referring to Figure 2, provided herein is a transport system having a hybrid neural network 247 for optimizing a vehicle's powertrain 213, wherein at least two parts of the hybrid neural network 247 optimize different parts of the powertrain 213. An artificial intelligence system may control the powertrain components 215 based on behavioral models (such as physical models for energy conversion, electrodynamic models, hydrodynamic models, chemical models, etc., as well as mechanical models for the behavior of various dynamically interacting system components). For example, the AI ​​system may control the powertrain components 215 by manipulating powertrain behavioral parameters 260 to achieve a powertrain state 261. The AI ​​system may be trained to manipulate the powertrain components 215 by training on a resulting dataset (e.g., fuel efficiency, safety, rider satisfaction, etc.) and / or on a dataset of operator behavior (e.g., sensor sets, cameras, etc., or driver behavior sensed by a vehicle information system). In embodiments, a hybrid approach may be used in which one neural network optimizes one part of the powertrain (e.g., for gear shift operation) and another neural network optimizes another part (e.g., brakes, clutch engagement, or energy discharge and charging). Any of the powertrain components described throughout this disclosure can be controlled by a set of control commands consisting of outputs from at least one component of the hybrid neural network 247.

[0382] Figure 3 shows a set of states that are provided as input to and / or controlled by the expert system / AI system 336, and which may be used in conjunction with various systems and components in the various embodiments described herein. States 344 may include vehicle operating states 345, which include vehicle configuration states, component states, diagnostic states, performance states, location states, maintenance states, and many others, as well as user experience states 346, which include experience-specific states, user emotional states 366, satisfaction states 367, location states, content / entertainment states, and many others.

[0383] Figure 4 illustrates a range of parameters 430 that may be taken as input by an expert system or AI system 136 (Figure 1) or its components, as described throughout this disclosure, or that may be provided as output from such system and / or one or more sensors 125 (Figure 1), cameras 127 (Figure 1), or external systems. Parameters 430 may include one or more targets 431 or objectives (such as those to be optimized by the expert system / AI system, including iteration and / or machine learning), such as performance targets 433 relating to fuel efficiency, travel time, satisfaction, financial efficiency, safety, etc. Parameters 430 may include market feedback parameters 435 relating to price, availability, location, etc., of goods, services, fuel, electricity, advertising, content, etc. Parameters 430 may include rider state parameters 437, such as parameters relating to comfort 439, emotional state, satisfaction, goals, type of travel, fatigue, etc. Parameter 430 may include parameters for various traffic-related profiles, such as traffic profiles 440 (many such as location, direction, density, and pattern over time), road profiles 441 (many such as elevation, curvature, direction, and road surface conditions), and user profiles. Parameter 430 may also include routing parameters 442, such as current vehicle location, destination, waypoints, points of interest, type of trip, travel goals, required arrival time, desired user experience, and many others. Parameter 430 may also include satisfaction parameters 443 for riders (including drivers), fleet managers, advertisers, merchants, owners, operators, insurance companies, regulators, and others. Parameter 430 may also include operational parameters 444, which include a wide variety of those described throughout this disclosure.

[0384] Figure 5 illustrates a set of vehicle user interfaces 523. The vehicle user interface 523 may include electromechanical interfaces 568 such as a steering interface, brake interface, seats, windows, moonroof, glove box, etc. Interface 523 may also include various software interfaces (which may have touch panels, dials, knobs, buttons, icons, or other functions) such as a game interface 569, a navigation interface 570, an entertainment interface 571, a vehicle settings interface 572, a search interface 573, and an e-commerce interface 574. The vehicle interface may be used to provide input to, or controlled by, one or more AI systems / expert systems, as described in embodiments throughout this disclosure.

[0385] Figure 6 shows a set of interfaces between transport system components, including interfaces within the host system (such as managing vehicles or fleets of vehicles) and host interfaces 650 between the host system and one or more third-party and / or external systems. The interfaces also include third-party interfaces 655 and end-user interfaces 651 for users of the host system, including an in-vehicle interface that may be used by LiDAR as noted in relation to Figure 5, as well as user interfaces for others such as fleet managers, insurance companies, regulators, police, advertisers, merchants, content providers, and many others. The interfaces may also include merchant interfaces 652, such as merchants offering advertisements, content related to offerings, and one or more rewards to induce routing or other actions on the user's side. The interfaces may also include machine interfaces 653 such as application programming interfaces (APIs) 654, networking interfaces, peer-to-peer interfaces, connectors, brokers, extract-transform-load (ETL) systems, bridges, gateways, and ports. The interface may include one or more host interfaces on which the host can manage and / or configure one or more of the many embodiments described herein, such as configuring neural network components, setting weights for models, setting one or more goals or objectives, and setting reward parameters 656. The interface may include an expert system / AI system configuration interface 657, such as selecting one or more models 658, selecting and configuring datasets 659 (such as sensor data, external data, and other inputs described herein), AI selection 660 and AI configuration 661 (such as selecting neural network categories, parameter weights, etc.), expert system / AI system feedback selection 662 for learning, etc., and supervision configuration 663.

[0386] Figure 7 shows a data processing system 758 that can process data from various sources, including social media data source 769, weather data source 770, road profile source 771, traffic data source 772, media data source 773, sensor set 774, and many others. The data processing system may be configured to extract data, convert data into a suitable format (such as for use by an interface system, AI system / expert system, or other system), load it into a suitable location, normalize data, clean data, dedup data, store data (such as enabling queries), and perform a wide range of processing tasks as described throughout this disclosure.

[0387] Figure 8 illustrates a set of algorithms 849 that may be performed in connection with one or more of the many embodiments of the transport systems described herein. Algorithms 849 may take input from and provide output to a set of AI systems / expert systems, such as many of the types described herein, and be managed by them. Algorithms 849 may include one or more genetic algorithms 875, such as an algorithm for providing or managing user satisfaction 874, or for determining a preferred state, parameter, or combination of state / parameters in connection with the optimization of one or more of the systems described herein. Algorithms 849 may include a vehicle routing algorithm 876, which may be sensitive to various vehicle operation parameters, user experience parameters, or other states, parameters, profiles, etc., described herein, and various goals or objectives. Algorithms 849 may include an object detection algorithm 876. Algorithms 849 may include an energy calculation algorithm 877, such as for calculating energy parameters, for optimizing fuel usage, electricity usage, etc., or for optimizing refueling or recharging time, location, amount, etc. The algorithm may include prediction algorithms such as traffic prediction algorithm 879, traffic prediction algorithm 880, and algorithms for predicting other states or parameters of a traffic system as described throughout this disclosure.

[0388] In various embodiments, the transport system 111 described herein may include vehicles (including fleets and sets of other vehicles), as well as various infrastructure systems. The infrastructure systems may include Internet of Things systems (e.g., using traffic signals, utility poles, toll booths, signs and other roadside devices and systems located on or within roads, cameras and other sensors on or inside buildings, etc.), refueling and charging systems (service stations, charging stations, etc., and wireless charging systems using wireless power transfer, etc.) and many others.

[0389] The electrical, mechanical, and / or powertrain components of a vehicle described herein may include a wide range of systems, including transmissions, gear systems, clutch systems, brake systems, fuel systems, lubrication systems, steering systems, suspension systems, lighting systems (including emergency lighting as well as interior and exterior lighting), electrical systems, and various subsystems and components thereof.

[0390] The operating state and parameters of a vehicle may include the route, purpose of the trip, geolocation, direction, vehicle range, powertrain parameters, current gear, speed / acceleration, suspension profile (including various parameters such as each wheel), charge status of electric and hybrid vehicles, fuel status of fuel-powered vehicles, and many other things described throughout this disclosure.

[0391] The Rider and / or user experience states and parameters described through this disclosure may include emotional states, comfort states, psychological states (e.g., anxiety, tension, relaxation), wakefulness / sleep states, and / or states relating to satisfaction, alertness, health, wellness, one or more goals or objectives, and many other things. User experience parameters described herein may further include those relating to driving, braking, curve approach, seat position, window status, ventilation system, climate control, temperature, humidity, sound level, entertainment content type (e.g., news, music, sports, comedy, etc.), route selection (POI, scenery, new sites, etc.), and many other things.

[0392] In the embodiment, the route may be assigned various value parameters, such as value parameters that can be optimized to improve user experience or other factors under the control of an AI system / expert system. The parameters of a route's value may include speed, duration, on-time arrival, length (e.g., in miles), objectives (e.g., seeing a Point of Interest (POI), completing a task (e.g., completing a shopping list, completing a delivery schedule, completing a meeting), refueling or charging parameters, and game-based objectives. As one of many examples, a route can be assigned a value for task completion in a model and / or as input or feedback to an AI system or expert system configured to optimize the route. A user might indicate an objective, for example, to meet at least one of a set of friends over the weekend, by interacting with a user interface or menu that allows the user to set objectives. The route may be configured to increase the likelihood of a meeting (including through interaction with a system that includes location information of other vehicles and / or inputs that give an awareness of the friend's location, such as a social data feed) by intersecting with the predicted location of the friend (which may be predicted by a neural network or other AI system / expert system, as described throughout this disclosure) and by providing an in-vehicle message (or message to a mobile device) indicating the likelihood of a meeting.

[0393] Market feedback factors may be used to optimize various elements of a transport system as described through this disclosure, such as current and projected prices and / or costs (e.g., fuel, electricity, etc., and goods, services, and content available along the route and / or in the vehicle), current and projected capacity, supply and / or demand for one or more transport-related elements (fuel, electricity, charging capacity, maintenance, services, replacement parts, new or used vehicles, ride-sharing capacity, autonomous vehicle capacity or availability, etc.), and many others.

[0394] The in-vehicle or on-vehicle interface may include negotiation systems such as bidding systems, price negotiation systems, and reward negotiation systems. For example, a user may negotiate for a higher reward in exchange for agreeing to reroute to a merchant's location, or a user may raise the price they are willing to pay for fuel (which may be offered to a nearby refueling station that can meet that price), or so on. The output from the negotiation (agreement price, travel time, etc.) may automatically result in route reconfiguration, such as being governed by an AI system / expert system.

[0395] Rewards as described herein, particularly those offered by merchants or hosts, may include one or more coupons redeemable at a location, the granting of higher priority (such as in group routing of multiple vehicles), permission to use "high-speed lanes," priority in charging or refueling capabilities, and many others. Actions that lead to rewards in a vehicle may include playing games, downloading apps, driving to a location, taking pictures of a location or object, visiting a website, watching or listening to advertisements, watching videos, and many others.

[0396] In embodiments, an AI system / expert system may use or optimize one or more parameters for a charging plan, such as for charging the battery of an electric or hybrid vehicle. Parameters for a charging plan may include routing (e.g., routing to charging locations), the amount of charge or fuel to be provided, the time for charging, battery status, battery charge profile, time required for charging, value of charging, value indicator, market price, bids for charging, available supply capacity (e.g., within a geofence or within the range of a vehicle cluster), demand (e.g., based on detected charging / refueling status, based on requested demand), supply, and others. A neural network or other system (optionally, the hybrid system described herein) using a model or algorithm (e.g., a genetic algorithm) may provide a preferred and / or optimal charging plan for a vehicle or a set of vehicles based on the parameters (by being trained over a series of trials on results and / or by using a training set of human-created or human-supervised inputs). Other inputs may include prioritization for specific vehicles (e.g., for emergency responders or for persons prioritized in relation to the various embodiments described herein).

[0397] In embodiments, processors as described herein may include neural processing chips, such as those employing a fabric, such as a Lambda fabric. Such chips may have multiple cores, such as 256, each core configured to be neuronal with other cores on the same chip. Each core may constitute a microscale digital signal processor, and the fabric can enable the cores to easily connect to other cores on the chip. In embodiments, the fabric may connect a large number of cores (e.g., 500,000 or more cores) and / or chips, thereby facilitating use in computing environments requiring, for example, large neural networks, massively parallel computing, and large and complex conditional logic. In embodiments, low-latency fabrics are used, such as those with device-to-device, rack-to-rack, or other latency of 400 nanoseconds, 300 nanoseconds, 200 nanoseconds, 100 nanoseconds, or less. The chips may be low-power chips that can be powered by energy harvesting from the environment, energy harvesting from test signals, energy harvesting from onboard antennas, etc. In embodiments, the core may be configured to enable the application of a set of sparse matrix heterogeneous machine learning algorithms. The chip may execute an object-oriented programming language such as C++ or Java. In embodiments, the chip may be programmed to run each core on a different algorithm, thereby enabling algorithmic heterogeneity to enable one or more embodiments of the hybrid neural network described through this disclosure. The chip can thereby take multiple inputs from multiple data sources (e.g., one per core), perform massively parallel processing using a large set of different algorithms, and provide multiple outputs (one per core or one per set of cores, etc.).

[0398] In embodiments, the chip may include, or enable, a security fabric, such as a fabric for performing content inspection, packet inspection (against blacklists, whitelists, etc.), in addition to undertaking processing tasks such as neural networks and hybrid AI solutions.

[0399] In embodiments, the platform described herein includes, and may be integrated with or connected to, a system for robotic process automation (RPA), thereby training an artificial intelligence / machine learning system with a training set of data consisting of tracking and recording a set of human interactions as a human interacts with a set of interfaces, such as graphical user interfaces (e.g., by interaction with a mouse, trackpad, keyboard, touchscreen, joystick, remote control device), audio system interfaces (e.g., by microphone, smart speaker, voice response interface, intelligent agent interface (e.g., Siri and Alexa)), human-machine interfaces (e.g., robotic systems, prosthetics, cybernetic systems, exoskeleton systems, wearables (including clothing, headgear, headphones, watches, wristbands, glasses, armbands, torso bands, belts, rings, necklaces and other accessories)), physical or mechanical interfaces (e.g., buttons, dials, toggles, knobs, touchscreens, levers, handles, steering systems, wheels), optical interfaces (including those triggered by eye tracking, facial recognition, gesture recognition, emotion recognition, etc.). Sensor-enabled interfaces (including 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 capable of recognizing thoughts, gestures (face, hands, posture, etc.), speech, etc.). In addition to tracking and recording human interactions, RPA systems can also track and record a range of states, actions, events, and results that occur within, from, or about systems and processes in which humans are involved.For example, an RPA system may record mouse clicks on video frames that occur during a human review of a video, such as highlighting points of interest in the video, tagging objects in the video, retrieving parameters (size, dimensions, etc.), or performing other operations on the video within a graphical user interface. The RPA system can also record the state and events of the system or process, for example, which elements were the subject of the interaction, what the state of the system was before, during, and after the interaction, and what output was provided or what results were achieved by the system. Through a large training set of observing human interactions and the state, events, and results of the system, an RPA system can learn to interact with the system in a way that mimics that of a human. Learning may be enhanced by training and monitoring, such as when a human corrects the RPA system as it attempts to perform actions that a human would have done (e.g., tagging the correct object, correctly labeling an item, selecting the correct button to initiate the next step in a process), and the RPA system can become increasingly effective at replicating actions that a human would have done during the series of trials. Learning may include deep learning, such as by reinforcing learning based on successful outcomes (e.g., based on successful process completion, financial yield, and many other outcome measures described throughout this disclosure). In embodiments, the RPA system may be seeded with a set of expert human interactions during the learning phase so that the RPA system begins to be able to replicate expert interactions with the system. For example, an expert driver's interaction with a robotic system such as a remotely controlled vehicle or UAV may be recorded along with information about the vehicle's state (e.g., surrounding environment, navigation parameters, and purpose) so that the RPA system can learn to drive the vehicle in a way that reflects the same choices as the expert driver.After being instructed to replicate the human skills or expertise of an expert, the RPA system may transition to a deep learning mode, where it further improves based on a series of results, such as being configured to try some level of variation in its approach (for example, tracking results (with feedback) so that the RPA system can learn to surpass the expertise of a human expert through variation / experimentation (which may be randomization, rule-based, etc., such as using genetic programming techniques, random walk techniques, random forest techniques, etc.) and selection). Thus, the RPA system can be given a seed that is very effective for artificial intelligence, such as providing a seed model or system that can learn from human experts, acquire expertise in interacting with systems or processes, facilitate process automation (for example, by taking over parts of more repetitive tasks, including those that require consistent execution of acquired skills), and improve through machine learning with feedback on the results of the system or process.

[0400] RPA systems can be particularly valuable in situations where human expertise or knowledge is acquired through training and experience, and where the human brain and sensory systems are particularly adapted and evolved to solve computationally difficult or highly complex problems. Accordingly, in embodiments, an RPA system may be used to learn to undertake the following in particular: visual pattern recognition tasks relating to various systems, processes, workflows and environments described herein (e.g., recognizing the meaning of dynamic interactions of objects or entities in a video stream (e.g., understanding what happens when humans and objects interact in a video)), recognizing the significance of visual patterns (e.g., recognizing objects, structures, defects, and states in a photograph or X-ray image), tagging relevant objects in a visual pattern (e.g., tagging or labeling objects by type, category, or specific identity (e.g., person recognition)), displaying metrics in a visual pattern (e.g., dimensions of objects shown by clicking on dimensions in an X-ray image), labeling activities in a visual pattern by category (e.g., what work processes are being performed) Dolphins, etc. Recognition of patterns that appear as signals (e.g., waves or similar patterns in the frequency domain, time domain, or other signal processing representations); prediction of future states based on current states (e.g., predicting future states based on current states (e.g., predicting the movement of flying or rolling objects, predicting the next action of a human in a process, predicting the next step of a machine, predicting a human reaction to an event, and many others)); recognition and prediction of emotional states and reactions (e.g., based on facial expressions, posture, body language, etc.); application of heuristics to achieve a desirable state without deterministic calculation (e.g., selecting a favorable strategy in sports or games, selecting a business strategy, selecting a negotiation strategy, pricing a product, developing a message to promote a product or idea, generating creative content, recognizing a favorable style or fashion, and many others)); and any many other things.In embodiments, an RPA system can automate workflows involving visual inspection of people, systems, and objects (including internal components); workflows involving the execution of software tasks involving sequential interaction with a series of screens in a software interface; workflows involving the remote control of robots and other systems and devices; workflows involving content creation (e.g., selection, editing, and arrangement); and many other workflows involving content creation (such as content selection, editing, and ordering); financial decision-making and negotiation (such as setting prices and conditions for financial transactions); and decision-making (such as selecting the optimal configuration of a system or subsystem, or selecting the optimal path or sequence of actions in workflows, processes, and other activities involving dynamic decision-making).

[0401] In embodiments, the RPA system may use a set of IoT devices and systems (such as cameras and sensors) to track and record human behavior and interactions with various interfaces and systems in the 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). Thus, the RPA system (or a set of RPA systems dedicated to automating various processes and workflows) can be trained to achieve processes and workflows in a way that reflects and mimics accumulated human expertise, and ultimately improve the results of that human expertise through further machine learning, including various entities (human and non-human), systems, processes, applications (e.g., software applications used to enable workflows), states, events, and results.

[0402] Referring to Figure 9, an embodiment provided herein provides a transport system 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 in order to determine at least one optimized operating state. In the embodiment, the genetic algorithm 975 takes inputs related to at least one vehicle performance parameter 982 and at least one lidar state 937.

[0403] Embodiments provided herein are transport systems 911 comprising a vehicle 910 having a vehicle operating state 945, and an artificial intelligence system 936 that executes a genetic algorithm 975 that generates 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 an optimized set of vehicle parameter values ​​for future operation of the vehicle 910 based on the evaluations.

[0404] In an embodiment, the vehicle operating state 945 includes the rider state 937 of the vehicle's rider. In an embodiment, at least one optimized vehicle operating state includes the optimized rider state. In an embodiment, the genetic algorithm 975 is to optimize the rider state. In an embodiment, evaluating according to a set of measures 983 is to determine the rider state corresponding to the vehicle parameter value 984.

[0405] In an embodiment, the vehicle operating state 945 includes the state of the vehicle's rider. In an embodiment, the vehicle parameter value set 984 includes a set of vehicle performance control values. In an embodiment, at least one optimized vehicle operating state includes an optimized state of vehicle performance. In an embodiment, the genetic algorithm 975 is to optimize the state of the rider and the state of vehicle performance. In an embodiment, evaluating according to a set of measures 983 is to determine the state of the rider and the state of vehicle performance corresponding to the vehicle performance control values.

[0406] In an embodiment, the vehicle parameter value set 984 includes a vehicle performance control value set. In an embodiment, at least one optimized vehicle operating state includes an optimized state of vehicle performance. In an embodiment, the genetic algorithm 975 is to optimize the state of vehicle performance. In an embodiment, evaluating according to the set of measures 983 is to determine the state of vehicle performance corresponding to the vehicle performance control values.

[0407] In an embodiment, the set of vehicle parameter values ​​984 includes rider occupancy parameter values. In an embodiment, the rider occupancy parameter values ​​affirm the presence of a rider in the vehicle 910. In an embodiment, the vehicle operating state 945 includes the rider state 937 of the vehicle's rider. In an embodiment, at least one optimized vehicle operating state includes the rider's optimized state. In an embodiment, the genetic algorithm 975 is to optimize the rider state. In an embodiment, evaluating according to the set of measures 983 is to determine the rider state corresponding to the vehicle parameter value 984. In an embodiment, the rider state includes rider satisfaction parameters. In an embodiment, the rider state includes inputs representing the rider. In an embodiment, the inputs representing the rider are selected from the group consisting of rider state parameters, rider comfort parameters, rider emotional state parameters, rider satisfaction parameters, rider goal parameters, travel classifications, and combinations thereof.

[0408] In an embodiment, the vehicle parameter value set 984 includes a set of vehicle performance control values. In an embodiment, at least one optimized vehicle operating state includes an optimized state of vehicle performance. In an embodiment, the genetic algorithm 975 is to optimize the rider state and the vehicle performance state. In an embodiment, evaluation according to the set of measures 983 is to determine the rider state and the vehicle performance state corresponding to the vehicle performance control values. In an embodiment, a set of vehicle parameter values ​​984 includes a set of vehicle performance control values. In an embodiment, at least one optimized vehicle operating state includes an optimized state of vehicle performance. In an embodiment, the genetic algorithm 975 is to optimize the vehicle performance state. In an embodiment, evaluation according to the set of measures 983 is to determine the vehicle performance state corresponding to the vehicle performance control values.

[0409] In embodiments, the set of vehicle performance control values ​​is selected from the group consisting of fuel efficiency, travel duration, vehicle wear, vehicle manufacturer, vehicle model, vehicle energy consumption profile, fuel capacity, real-time fuel level, charge capacity, charge capability, regenerative braking state, and combinations thereof. In embodiments, at least a portion of the set of vehicle performance control values ​​is supplied from at least one of an onboard diagnostic system, a telemetry system, a software system, sensors located in the vehicle, and a system outside the vehicle 910. In embodiments, the set of measures 983 relates to a set of vehicle operation criteria. In embodiments, the set of measures 983 relates to a set of rider satisfaction criteria. In embodiments, the set of measures 983 relates to a combination of vehicle operation criteria and rider satisfaction criteria. In embodiments, each evaluation uses feedback indicating an impact on at least one of the vehicle performance state and the rider state.

[0410] Embodiments provided herein are systems for transport 911, comprising an artificial intelligence system 936 that processes inputs representing the vehicle state and inputs representing the rider state 937 of a rider occupying the vehicle in a genetic algorithm 975 to optimize a set of vehicle parameters that affect the vehicle state or 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 indicating an effect on at least one of the vehicle operating state 945 and the rider state 937. In embodiments, the input representing the rider state 937 indicates that the rider is absent from the vehicle 910. In embodiments, the vehicle state includes the vehicle operating state 945. In embodiments, the vehicle parameters in the set of vehicle parameters include the vehicle performance parameter 982. In embodiments, the genetic algorithm 975 is to optimize a set of vehicle parameters for the rider state.

[0411] In embodiments, optimizing a set of vehicle parameters is a response to the genetic algorithm 975 identifying at least one vehicle parameter that results in a preferred rider state. In embodiments, the genetic algorithm 975 optimizes the set of vehicle parameters for vehicle performance. In embodiments, the genetic algorithm 975 optimizes the set of vehicle parameters for rider states and optimizes the set of vehicle parameters for vehicle performance. In embodiments, optimizing a set of vehicle parameters is a response to the genetic algorithm 975 identifying at least one of a preferred vehicle operating state and a preferred vehicle performance that maintains a 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 includes the genetic algorithm 975. In embodiments, the selection of the neural network is based on structured competition among a plurality of different neural networks. In embodiments, the genetic algorithm 975 facilitates training a neural network to process interactions between a plurality of vehicle operating systems and riders to generate an optimized set of vehicle parameters.

[0412] In embodiments, a set of inputs related to at least one vehicle parameter is provided by at least one of an onboard diagnostic system, a telemetry system, a sensor located in the vehicle, and an external system. In embodiments, the input representing the rider state 937 consists of at least one of comfort, emotional state, satisfaction, goals, travel classification, or fatigue. In embodiments, the input representing the rider state 937 reflects at least one satisfaction parameter from among the driver, fleet manager, advertiser, merchant, owner, operator, insurance company, and regulator. In embodiments, the input representing the rider state 937 consists of user-related inputs that, when processed by a cognitive system, result in the rider state 937.

[0413] Referring to Figure 10, an embodiment provided herein provides a transport system 1011 having a hybrid neural network 1047 for optimizing the operating state of a continuously variable powertrain 1013 of a vehicle 1010. In an embodiment, at least a portion of the hybrid neural network 1047 operates to classify the state of the vehicle 1010, and another portion of the hybrid neural network 1047 operates to optimize at least one operating parameter 1060 of the transmission 1019. In an embodiment, the vehicle 1010 may be an autonomous vehicle. For example, the first part 1085 of the hybrid neural network may classify the vehicle 1010 into high-traffic conditions (e.g., by using LIDAR, RADAR, etc., to indicate 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, etc.), adverse weather conditions (e.g., by taking input indicating wet roads (e.g., using a visual-based system), precipitation (e.g., determined by radar), presence of ice (e.g., temperature sensing, visual-based sensing), hail (e.g., impact detection, sound sensing), lightning (e.g., a visual-based system, a sound-based system, etc.), etc.). Once classified, another neural network 1086 (optionally of another type) may optimize vehicle operating parameters based on the classified conditions, such as putting the vehicle 1010 into a safe driving mode (e.g., by providing automatic braking earlier and more aggressively than in good weather, such as by providing forward sensing warnings at a greater distance and / or lower speed than in good weather).

[0414] Embodiments provided herein are systems 1011 for transport, comprising a hybrid neural network 1047 for optimizing the operating states of a continuously variable powertrain 1013 of a vehicle 1010. In embodiments, a portion 1085 of the hybrid neural network 1047 operates to classify states 1044 of the vehicle 1010 and thereby generate classified states of the vehicle, and another portion 1086 of the hybrid neural network 1047 operates to optimize at least one operating parameter 1060 of the transmission portion 1019 of the continuously variable powertrain 1013.

[0415] In an embodiment, the transport system 1011 further comprises an artificial intelligence system 1036 operating on at least one processor 1088, an artificial intelligence system 1036 operating a portion 1085 of a hybrid neural network 1047 that operates to classify the state of the vehicle, and an artificial intelligence system 1036 operating another portion 1086 of the hybrid neural network 1047 to optimize at least one operating parameter 1087 of the transmission portion 1019 of the continuously variable powertrain 1013 based on the classified state of the vehicle. In an embodiment, the vehicle 1010 constitutes a system for automating at least one control parameter of the vehicle. In an embodiment, the vehicle 1010 is at least a semi-autonomous vehicle. In an embodiment, the vehicle 1010 is automatically routed. In an embodiment, the vehicle 1010 is an autonomous driving vehicle. In the embodiment, the classified states of the vehicle are vehicle maintenance state, vehicle health state, vehicle operation state, vehicle energy utilization state, vehicle charging state, vehicle satisfaction state, vehicle component state, vehicle subsystem state, vehicle powertrain system state, vehicle brake system state, vehicle clutch system state, vehicle lubrication system state, vehicle traffic infrastructure system state, or vehicle occupant state. In the embodiment, at least a portion of the hybrid neural network 1047 is a convolutional neural network.

[0416] Figure 11 shows a method 1100 for optimizing the operation of a continuously variable vehicle powertrain of a vehicle, according to embodiments of the systems and methods disclosed herein. In 1102, the method comprises running a first network of a hybrid neural network on at least one processor, the first network classifying a plurality of operating states of the vehicle. In embodiments, at least some of the operating states are based on the state of the vehicle's continuously variable powertrain. In 1104, the method comprises running a second network of a hybrid neural network on at least one processor, the second network processing inputs describing at least one detected state related to the vehicle and the vehicle's occupants for at least one of the plurality of classified driving states of the vehicle. In embodiments, the processing of inputs by the second network results in the optimization of at least one driving parameter of the vehicle's continuously variable powertrain for the plurality of driving states of the vehicle.

[0417] Referring together to Figures 10 and 11, in an embodiment, the vehicle comprises an artificial intelligence system 1036, and the method further includes automating at least one control parameter of the vehicle by the artificial intelligence system 1036. In an embodiment, the vehicle 1010 is at least a semi-autonomous vehicle. In an embodiment, the vehicle 1010 is automatically routed. In an embodiment, the vehicle 1010 is an autonomous vehicle. In an embodiment, the method further includes optimizing the operating state of the vehicle's continuously variable powertrain 1013 based on an optimized at least one operating parameter 1060 of the continuously variable powertrain 1013 by adjusting at least one other operating parameter 1087 of the transmission portion 1019 of the continuously variable powertrain 1013 by the artificial intelligence system 1036.

[0418] In an embodiment, the method further comprises optimizing the operating state of the continuously variable powertrain 1013 by processing social data from multiple social data sources using an artificial intelligence system 1036. In an embodiment, the method further comprises optimizing the operating state of the continuously variable powertrain 1013 by processing data supplied from a stream of data from an unstructured data source using an artificial intelligence system 1036. In an embodiment, the method further comprises optimizing the operating state of the continuously variable powertrain 1013 by processing data supplied from a wearable device using an artificial intelligence system 1036. In an embodiment, the method further comprises optimizing the operating state of the continuously variable powertrain 1013 by processing data supplied from an in-vehicle sensor using an artificial intelligence system 1036. In an embodiment, the method further comprises optimizing the operating state of the continuously variable powertrain 1013 by processing data supplied from a rider's helmet using an artificial intelligence system 1036.

[0419] In an embodiment, the method further comprises optimizing the operating state of the continuously variable powertrain 1013 by processing data supplied from the rider headgear using an artificial intelligence system 1036. In an embodiment, the method further comprises optimizing the operating state of the continuously variable powertrain 1013 by processing data supplied from the rider voice system using an artificial intelligence system 1036. In an embodiment, the method further comprises using an artificial intelligence system 1036 to operate a third network of the hybrid neural network 1047 to predict the state of the vehicle at least in part on at least one of a plurality of classified operating states of the vehicle and at least one operating parameter of the transmission 1019. In an embodiment, the first network of the hybrid neural network 1047 constitutes a structure-adaptive network for adapting the structure of the first network in response to the results of operating the first network of the hybrid neural network 1047. In an embodiment, the first network of the hybrid neural network 1047 processes a plurality of social data from a social data source to classify a plurality of operating states of the vehicle.

[0420] In an embodiment, at least a portion of the hybrid neural network 1047 is a convolutional neural network. In an embodiment, at least one of the classified operating states of the vehicle is: vehicle maintenance state, or vehicle health state. In an embodiment, at least one of the classified states of the vehicle is: vehicle operating state, vehicle energy utilization state, vehicle charging state, vehicle satisfaction state, vehicle component state, vehicle subsystem state, vehicle powertrain system state, vehicle brake system state, vehicle clutch system state, vehicle lubrication system state, or vehicle traffic infrastructure system state. In an embodiment, at least one of the classified states of the vehicle is the vehicle driver state. In an embodiment, at least one of the classified states of the vehicle is the vehicle rider state.

[0421] Referring to Figure 12, in an embodiment, provided herein is a transport system 1211 having a cognitive system for routing at least one vehicle 1210 within a set of vehicles 1294 based on route parameters determined by facilitating negotiation between a given set of vehicles. In an embodiment, the negotiation accepts input regarding the value that at least one rider attributes to at least one parameter 1230 of the route 1295. A user 1290 may express value by an action (e.g., performing an action that reflects or indicates the value attributed to arriving on time, following a given route 1295, etc.) or by providing or offering value (e.g., currency, tokens, points, cryptocurrency, rewards, etc.) through a user interface that evaluates one or more parameters (e.g., any of the parameters pointed out throughout). For example, user 1290 may negotiate a preferred route by providing the system with a token that will be given if user 1290 arrives at a specified time, while another may offer to accept the token in exchange for taking an alternative route (thus reducing congestion). Therefore, the artificial intelligence system may optimize combinations of offers to provide rewards or to undertake actions in response to rewards, so that the reward system optimizes a set of outcomes. Negotiations may include explicit negotiations, for example, in which a driver offers to reward a driver ahead of them on the road in exchange for temporarily leaving the route as the driver passes.

[0422] Embodiments provided herein are characterized by a system 1211 for transport, a cognitive system for routing at least one vehicle 1210 within a set of vehicles 1294 based on routing parameters determined by facilitating negotiation between a designated set of vehicles, wherein the negotiation accepts input from at least one user 1290 regarding values ​​at least one parameter of a route 1295.

[0423] Figure 13 shows a negotiation-based vehicle routing method 1300 according to embodiments of the systems and methods disclosed herein. In 1302, the method includes facilitating the negotiation of route adjustment values ​​for a plurality of parameters used by a vehicle routing system to route at least one vehicle in a set of vehicles. In 1304, the method includes determining parameters in a plurality of parameters to optimize at least one result based on the negotiation.

[0424] Referring to Figures 12 and 13, in an embodiment, user 1290 is the administrator for a set of roads used by at least one vehicle 1210 in a set of vehicles 1294. In an embodiment, user 1290 is the administrator for a fleet of vehicles including the set of vehicles 1294. In an embodiment, the method further includes providing user 1290 with respect to the set of vehicles 1294 a set of provided user-indicated values ​​for a plurality of parameters 1230. In an embodiment, the route adjustment value 1224 is at least partially based on the provided set of user-indicated values ​​1297. In an embodiment, the route adjustment value 1224 is further based on at least one user response to the provision. In an embodiment, the route adjustment value 1224 is at least partially based on the provided set of user-indicated values ​​1297 and at least one response to it by at least one user of the set of vehicles 1294. In an embodiment, the determined parameters facilitate the adjustment of at least one route 1295 for vehicle 1210 in the set of vehicles 1294. In an embodiment, route adjustment includes prioritizing parameters determined for use by a vehicle routing system.

[0425] In an embodiment, facilitating negotiation includes facilitating negotiation on the price of a service. In an embodiment, facilitating negotiation includes facilitating negotiation on the price of fuel. In an embodiment, facilitating negotiation includes facilitating negotiation on the price of charging. In an embodiment, facilitating negotiation includes facilitating negotiation on a reward for taking routing action.

[0426] Embodiments provided herein include a transport system 1211 for negotiation-based vehicle routing. It comprises a route adjustment negotiation system 1289 in which a user 1290 of a set of users 1291 negotiates a route adjustment value 1224 for at least one of a plurality of parameters 1230 that a vehicle routing system 1292 uses to route at least one vehicle 1210 in a vehicle set 1294, and a user route optimization circuit 1293 that optimizes a portion of the route 1295 for at least one user 1290 of the vehicle set 1294 based on the route adjustment value 1224 for at least one of the plurality of parameters 1230. In embodiments, the route adjustment value 1224 is at least partially based on a user instruction value 1297 and at least one negotiation response thereto by at least one user of the vehicle set 1294. In embodiments, the transport system 1211 further comprises a vehicle-based route negotiation interface into which the user instruction value 1297 for a plurality of parameters 1230 used by the vehicle routing system is taken up. In one embodiment, user 1290 is the rider of at least one vehicle 1210. In another embodiment, user 1290 is the administrator of a set of roads 1294 used by at least one vehicle 1210.

[0427] In an embodiment, user 1290 is an administrator of a fleet of vehicles including a set of vehicles 1294. In an embodiment, at least one of a plurality of parameters 1230 facilitates the coordination of a route 1295 for at least one vehicle 1210. In an embodiment, coordinating a route 1295 involves prioritizing parameters determined for use by the vehicle routing system. In an embodiment, at least one user-indicated value 1297 is reduced to at least one of the plurality of parameters 1230 via an interface to facilitate the expression of an evaluation of one or more route parameters. In an embodiment, a vehicle-based route negotiation interface facilitates the expression of a ranking of one or more route parameters. In an embodiment, a user-indicated value 1297 is derived from the actions of user 1290. In an embodiment, a vehicle-based route negotiation interface facilitates the translation of user behavior into a user-indicated value 1297. In an embodiment, user behavior reflects a value assigned to at least one parameter used by the vehicle routing system to influence the route 1295 of at least one vehicle 1210 in the vehicle set 1294. In an embodiment, a user-indicated value shown by at least one user 1290 correlates to a value item provided by user 1290. In an embodiment, a value item is provided by user 1290 through the provision of a value item in exchange for the routing result based on at least one parameter. In an embodiment, the negotiation of a route adjustment value 1224 includes providing a value item to users of the vehicle set 1294.

[0428] Referring to Figure 14, an embodiment provided herein provides a transport system 1411 having a cognitive system for routing at least one vehicle 1410 within a set of vehicles 1494 based on routing parameters determined by facilitating coordination among a given set of vehicles 1498. In the embodiment, coordination is achieved by taking at least one input from at least one game-based interface 1499 for the rider of a vehicle. The game-based interface 1499 may include rewards for undertaking gamified behaviors (i.e., game activities 14101) that provide an additional benefit. For example, the rider of vehicle 1410 may be rewarded for routing vehicle 1410 to a point of interest off the highway (such as collecting coins or capturing items), and the rider's departure would free up space for other vehicles attempting to achieve other objectives, such as on-time arrival. For example, a game like Pokémon Go® may be configured to indicate the presence of rare Pokémon® creatures to attract traffic to locations away from congested areas. Alternatively, rewards (e.g., currency, cryptocurrency, etc.) that can be pooled to attract users away from congested roads may be offered.

[0429] Embodiments provided herein are systems 1411 for transport, comprising a cognitive system for routing at least one vehicle 1410 within a vehicle set 1494 based on a set of routing parameters 1430 determined by facilitating coordination between designated vehicle sets 1498, wherein the coordination is achieved by taking at least one input from at least one game-based interface 1499 to a user 1490 of the vehicle 1410 within the vehicle set 1498.

[0430] In one embodiment, the transport system comprises a vehicle routing system 1492 that routes at least one vehicle 1410 based on a set of routing parameters 1430, and a game-based interface 1499 that directs a user 1490 to a routing preference 14100 for at least one vehicle 1410 in a set of vehicles 1494 in order to undertake a game activity 14101 provided in the game-based interface 1499, the game-based interface 1499 being for instructing the user 1490 to undertake a set of preferred routing options based on a set of routing parameters 1430. As used herein, “to route” means to select a route 1495.

[0431] In an embodiment, the vehicle routing system 1492 takes into account the routing preference 14100 of user 1490 when routing at least one vehicle 1410 within the vehicle set 1494. In an embodiment, the game-based interface 1499 is positioned for in-vehicle use, as shown in Figure 14 by lines extending from the game-based interface into a box for vehicle 1. In an embodiment, user 1490 is the rider of at least one vehicle 1410. In an embodiment, user 1490 is the administrator of the set of roads used by at least one vehicle 1410 in the vehicle set 1494. In an embodiment, user 1490 is the administrator of 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 time, preferred route, fuel efficiency, pollution reduction, accident avoidance, bad weather avoidance, bad road conditions avoidance, fuel consumption reduction, carbon dioxide emission reduction, local noise reduction, high crime area avoidance, collective satisfaction, speed limit, toll road avoidance, city road avoidance, undistributed highway avoidance, left turn avoidance, and driver-operated vehicle avoidance. In embodiments, the game activity 14101 provided by the game-based interface 1499 includes contests. In embodiments, the game activity 14101 provided by the game-based interface 1499 includes entertainment games.

[0432] In an embodiment, the game activity 14101 provided by the game-based interface 1499 includes a competitive game. In an embodiment, the game activity 14101 provided by the game-based interface 1499 includes a strategy game. In an embodiment, the game activity 14101 provided by the game-based interface 1499 includes a scavenger hunt. In an embodiment, a set of favorable route selections configures the vehicle routing system 1492 to achieve a fuel efficiency target. In an embodiment, a set of favorable route selections configures the vehicle route control system 1492 to achieve a traffic volume reduction objective. In an embodiment, a set of favorable route selections configures the vehicle route control system 1492 to achieve a pollution reduction objective. In an embodiment, a set of favorable route selections configures the vehicle route selection system 1492 to achieve a carbon footprint reduction objective.

[0433] In embodiments, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves the objective of reducing neighborhood noise. In embodiments, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves the objective of collective satisfaction. In embodiments, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves the objective of avoiding accident sites. In embodiments, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves the objective of avoiding high-crime areas. In embodiments, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves the objective of reducing traffic congestion. In embodiments, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves the objective of avoiding bad weather.

[0434] In an embodiment, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves a maximum travel time objective. In an embodiment, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves a maximum speed limit objective. In an embodiment, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves a toll road avoidance objective. In an embodiment, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves an urban road avoidance objective. In an embodiment, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves an undivided highway avoidance objective. In an embodiment, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves a left turn avoidance objective. In an embodiment, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves a driver-operated vehicle avoidance objective.

[0435] Figure 15 shows a method 1500 of game-based cooperative vehicle routing according to embodiments of the systems and methods disclosed herein. In 1502, the method includes presenting game activities in a game-based interface that influence vehicle route preferences. In 1504, the method includes receiving user responses to the presented game activities via the game-based interface. In 1506, the method includes adjusting the user's route preferences in response to the received responses. In 1508, the method includes determining at least one vehicle routing parameter to be used to route the vehicles in order to reflect the adjusted routing preferences for routing the vehicles. In 1508, the method includes routing vehicles in a set of vehicles in a vehicle routing system in response to at least one determined vehicle routing parameter adjusted to reflect adjusted routing preferences, wherein the vehicle routing includes adjusting the determined routing parameter for at least one vehicle in the set of vehicles.

[0436] Referring to Figures 14 and 15, in an embodiment, the method further comprises a game-based interface 1499 indicating a reward value 14102 for accepting a game activity 14101. In an embodiment, the game-based interface 1499 further comprises a routing preference negotiation system 1436 for the rider to negotiate the reward value 14102 for accepting the game activity 14101. In an embodiment, the reward value 14102 is the result of pooling value contributions from riders in a set of vehicles. In an embodiment, at least one routing parameter 1430 used by a vehicle routing system 1492 to route vehicles 1410 in a set of vehicles 1494 is associated with a game activity 14101, and user acceptance of the game activity 14101 adjusts at least one routing parameter 1430 (e.g., by a routing adjustment value 1424) to reflect routing preferences. In an embodiment, the user response to the presented game activity 14101 is obtained from user interaction with the game-based interface 1499. In an embodiment, at least one routing parameter used by the vehicle routing system 1492 to route the vehicles 1410 in the vehicle set 1494 includes at least one of the following: traffic congestion, desired arrival time, preferred route, fuel efficiency, pollution reduction, accident avoidance, avoidance of bad weather, avoidance of poor road conditions, fuel consumption reduction, carbon dioxide emission reduction, local noise reduction, avoidance of high-crime areas, crowd satisfaction, speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undistributed highways, avoidance of left turns, and avoidance of vehicles being driven.

[0437] In an embodiment, the game activity 14101 presented in the game-based interface 1499 includes a contest. In an embodiment, the game activity 14101 presented in the game-based interface 1499 includes an entertainment game. In an embodiment, the game activity 14101 presented in the game-based interface 1496 includes a competitive game. In an embodiment, the game activity 14101 presented in the game-based interface 1499 includes a strategy game. In an embodiment, the game activity 14101 presented in the game-based interface 1499 includes a scavenger hunt. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a fuel efficiency target. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a reduced traffic objective.

[0438] In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a reduced pollution objective. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a reduced carbon footprint objective. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a neighborhood noise reduction objective. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a collective satisfaction objective. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a goal of avoiding accident sites. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a goal of avoiding high-crime areas. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a goal of reducing traffic congestion.

[0439] In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of avoiding bad weather. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of achieving the maximum travel time. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of achieving the maximum speed limit. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of avoiding toll roads. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of avoiding urban roads. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of avoiding undivided highways. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of avoiding left turns. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of avoiding vehicles operated by the driver.

[0440] In embodiments, provided herein is a transport system 1611 having a cognitive system for routing at least one vehicle, the routing being at least partially determined by processing at least one input from a rider interface in which the rider can receive a reward 16102 by undertaking a certain action while riding in the vehicle. In embodiments, the rider interface may display a set of rewards to pursue, a set of rewards available for performing various actions (e.g., by interacting with a touch panel or audio interface), such as enabling the rider to use actions that result in rewards for the vehicle's navigation system (or a rideshare system at least partially controlled by the user 1690) or the routing system 1692 of an autonomous vehicle to take control of the routing. For example, selecting a reward for joining a site may result in sending a signal to the navigation or routing system 1692 to set the site as an intermediate destination. As another example, indicating an intention to view part of content may cause the routing system 1692 to select a route that allows sufficient time to view or listen to the content.

[0441] Embodiments provided herein include a cognitive system 1611 for routing at least one vehicle 1610, the routing being at least partially based on processing at least one input from a rider interface, and the reward 16102 being made available to the rider in response to the rider undertaking a predetermined action while riding in at least one vehicle 1610.

[0442] Embodiments provided herein include a transport system 1611 for reward-based cooperative vehicle routing, comprising: a reward-based interface 16104 for providing rewards 16102, through which a user 1690 associated with a vehicle set 1694 indicates the user 1690's routing preferences related to the rewards 16102 by responding to the rewards 16102 provided by the reward-based interface 16104; a reward-providing response processing circuit 16105 for determining at least one user action resulting from the user's response to the rewards 16102 and the corresponding effect 16106 on at least one routing parameter 1630; and a vehicle routing system 1692 for governing the routing of the vehicle set 1694 using the user 1690's routing preferences 16100 and the corresponding effect on at least one routing parameter.

[0443] In an embodiment, user 1690 is the rider of at least one vehicle 1610 in a set of vehicles 1694. In an embodiment, user 1690 is the administrator for a set of roads used by at least one vehicle 1610 in a set of vehicles 1694. In an embodiment, user 1690 is the administrator for a fleet of vehicles including a set of vehicles 1694. In an embodiment, the reward-based interface 16104 is configured for in-vehicle use. In an embodiment, at least one routing parameter 1630 includes at least one of the following: congestion, desired arrival time, priority route, fuel efficiency, pollution reduction, accident avoidance, bad weather avoidance, bad road conditions avoidance, fuel consumption reduction, carbon dioxide emission reduction, local noise reduction, high crime area avoidance, collective satisfaction, speed limit, toll road avoidance, city road avoidance, undistributed highway avoidance, left turn avoidance, and driver-operated vehicle avoidance. In an embodiment, the vehicle routing system 1692 controls the routing of a set of vehicles to achieve a fuel efficiency target using the user 1690's routing preference and the corresponding effect on at least one routing parameter. In an embodiment, the vehicle route control system 1692 controls the route of a set of vehicles to achieve a traffic volume reduction objective using the user 1690's route control preference and the corresponding effect on at least one route control parameter. In an embodiment, the vehicle routing system 1692 controls the routing of a set of vehicles to achieve a reduced pollution objective using the user 1690's routing preference and the corresponding effect on at least one routing parameter. In an embodiment, the vehicle routing system 1692 controls the routing of a set of vehicles to achieve a carbon footprint reduction objective using the user 1690's routing preference and the corresponding effect on at least one routing parameter.

[0444] In an embodiment, the vehicle routing system 1692 controls the routing of a vehicle group to achieve the objective of reducing neighborhood noise by using the routing preferences of the user 1690 and the corresponding effects for at least one routing parameter. In an embodiment, the vehicle routing system 1692 uses the routing preferences of the user 1690 and the corresponding effects for at least one routing parameter to control the routing of a vehicle set to achieve the objective of collective satisfaction. In an embodiment, the vehicle route control system 1692 controls the route of a vehicle group to achieve the objective of avoiding accident sites by using the route control preferences of the user 1690 and the corresponding effects for at least one route control parameter. In an embodiment, the vehicle routing system 1692 controls the routing of a vehicle set to achieve the objective of avoiding high-crime areas by using the routing preferences of the user 1690 and the corresponding effects for at least one routing parameter. In one embodiment, the vehicle routing system 1692 controls the routing of a set of vehicles to achieve the objective of reducing traffic congestion, using the routing preferences of the user 1690 and the corresponding effects on at least one routing parameter.

[0445] In an embodiment, the vehicle route control system 1692 uses the user 1690's route control preferences and the corresponding effects for at least one route control parameter to control the route of a set of vehicles in order to achieve the objective of avoiding bad weather. In an embodiment, the vehicle route control system 1692 uses the user 1690's route control preferences and the corresponding effects for at least one route control parameter to control the route of a set of vehicles in order to achieve the objective of achieving maximum travel time. In an embodiment, the vehicle routing system 1692 uses the user 1690's routing preferences and the corresponding effects for at least one routing parameter to control the routing of a set of vehicles in order to achieve the objective of achieving maximum speed limit. In an embodiment, the vehicle route control system 1692 uses the user 1690's route control preferences and the corresponding effects for at least one route control parameter to control the route of a set of vehicles in order to achieve the objective of avoiding toll roads. In one embodiment, the vehicle routing system 1692 uses the routing preferences of the user 1690 and the corresponding effects on at least one routing parameter to control the routing of a set of vehicles in order to achieve the objective of avoiding urban roads.

[0446] In an embodiment, the vehicle route control system 1692 uses the user 1690's route control preferences and corresponding effects for at least one route control parameter to govern the route control of a set of vehicles in order to achieve the objective of avoiding undivided highways. In an embodiment, the vehicle routing system 1692 uses the user 1690's routing preferences and corresponding effects for at least one routing parameter to manage the routing of a set of vehicles in order to achieve the objective of avoiding left turns. In an embodiment, the vehicle routing system 1692 uses the user 1690's routing preferences and corresponding effects for at least one routing parameter to govern the routing of a set of vehicles in order to achieve the objective of avoiding driver-operated vehicles.

[0447] Figure 17 shows a reward-based coordinated vehicle routing method 1700 according to embodiments of the systems and methods disclosed herein. In 1702, the method includes receiving user responses related to a set of vehicles for rewards provided in a reward-based interface through a reward-based interface. In 1704, the method includes determining routing preferences based on user responses. In 1706, the method includes determining at least one user action resulting from the user's response to the reward. In 1708, the method includes determining the corresponding effect of at least one user action on at least one routing parameter. In 1702, the method includes governing the routing of a set of vehicles in response to routing preferences and the corresponding effect on at least one routing parameter.

[0448] In one embodiment, user 1690 is the rider of at least one vehicle 1610 in a set of vehicles 1694. In another embodiment, user 1690 is the administrator for a set of roads used by at least one vehicle 1610 in a set of vehicles 1694. In yet another embodiment, user 1690 is the administrator for a fleet of vehicles including a set of vehicles 1694.

[0449] In an embodiment, the reward-based interface 16104 is configured for in-vehicle use. In an embodiment, at least one routing parameter 1630 includes at least one of the following: congestion, desired arrival time, preferred route, fuel efficiency, pollution reduction, accident avoidance, avoidance of bad weather, avoidance of poor road conditions, fuel consumption reduction, carbon dioxide emission reduction, local noise reduction, avoidance of high-crime areas, collective satisfaction, speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undistributed highways, avoidance of left turns, and avoidance of vehicles being driven. In an embodiment, user 1690 responds to reward 16102 by accepting the reward 16102 offered by the reward-based interface 16104, rejecting the reward 16102 offered by the reward-based interface 16104, or ignoring the reward 16102 offered by the reward-based interface 16104. In an embodiment, user 1690 indicates routing preference by either accepting or rejecting the reward 16102 offered by the reward-based interface 16104. In this embodiment, user 1690 directs routing priorities by undertaking actions in at least one vehicle 1610 within the vehicle set 1694, which facilitates the transfer of reward 16102 to user 1690.

[0450] In an embodiment, the method further comprises transmitting a signal to the vehicle routing system 1692 via the reward-providing response processing circuit 16105 to select a vehicle route that allows sufficient time for user 1690 to perform at least one user action. In an embodiment, the method further comprises transmitting a signal to the vehicle routing system 1692 via the reward-providing response processing circuit 16105, the signal indicating a vehicle destination related to at least one user action, and the vehicle routing system 1692 adjusting the route of vehicle 1695 related to at least one user action to include the destination. In an embodiment, the reward 16102 is related to achieving a vehicle routing fuel efficiency target.

[0451] In embodiments, reward 16102 is related to achieving the objective of reduced traffic in vehicle routing. In embodiments, reward 16102 is related to achieving the objective of reduced pollution in vehicle routing. In embodiments, reward 16102 is related to achieving the objective of reduced carbon footprint in vehicle routing. In embodiments, reward 16102 is related to achieving the objective of reduced neighborhood noise in vehicle routing. In embodiments, reward 16102 is related to achieving the objective of collective satisfaction in vehicle routing. In embodiments, reward 16102 is related to achieving the objective of accident site avoidance in vehicle routing.

[0452] In embodiments, the reward 16102 is related to achieving the objective of vehicle routing to avoid high-crime areas. In embodiments, the reward 16102 is related to achieving the objective of reducing traffic congestion in vehicle routing. In embodiments, the reward 16102 is related to achieving the objective of vehicle routing to avoid bad weather. In embodiments, the reward 16102 is related to achieving the objective of vehicle routing to maximize travel time. In embodiments, the reward 16102 is related to achieving the objective of vehicle routing to maximize speed. In embodiments, the reward 16102 is related to achieving the objective of vehicle routing to avoid toll roads. In embodiments, the reward 16102 is related to achieving the objective of vehicle routing to avoid urban roads. In embodiments, the reward 16102 is related to achieving vehicle routing to avoid undivided highways. In embodiments, the reward 16102 is related to achieving the objective of vehicle routing to avoid left turns. In embodiments, the reward 16102 is related to achieving the objective of vehicle routing for driven vehicles.

[0453] Referring to Figure 18, an embodiment provided herein is a transportation system 1811 having a data processing system 1862 for predicting emerging transportation needs 18112 for a group of individuals, taking data 18114 from multiple social data sources 1869 and using a neural network 18108. Of the various social data sources 18107 described above, a large amount of data is available on social groups such as groups of friends, families, colleagues at work, club members, people with common interests or affiliations, and political groups. The expert system described above can be trained to predict the transportation needs of a group, for example, by using training datasets and / or models of human predictions, as described throughout, and by providing feedback on the results. For example, based on a discussion thread of a social group that is at least partially shown on a social network feed, it becomes clear that a group meeting or trip is taking place, and the system can predict when and where each participant will need to travel to attend (using indicators such as the location of each member and a set of travel destinations). Based on such predictions, the system can automatically identify and display transportation options, such as available public transport options, flight options, and rideshare options. Such options could include things that allow the group to share transportation, such as showing a route that would involve picking up a set of group members to travel together. Social media information could include posts, tweets, comments, chats, photos, etc., and could be processed as described above.

[0454] Embodiments provided herein include a transport system 1811 comprising a data processing system 1862 for taking data 18114 from a plurality of social data sources 1869 and using a neural network 18108 to predict new transport needs 18112 for a group of individuals 18110.

[0455] Figure 19 illustrates a method 1900 for predicting common transportation needs of a group, according to embodiments of the systems and methods disclosed herein. In 1902, the method comprises collecting social media source data relating to multiple individuals, the data being sourced from multiple social media sources. In 1904, the method comprises processing the data to identify subsets of multiple individuals that form social groups based on group affiliation references in the data. In 1906, the method comprises detecting keywords in the data that indicate transportation needs. In 1908, the method comprises identifying common transportation needs for subsets of multiple individuals using a neural network trained to predict transportation needs based on detected keywords.

[0456] Referring to Figures 18 and 19, in an embodiment, the neural network 18108 is a convolutional neural network 18113. In an embodiment, the neural network 18108 is trained on a model that facilitates matching phrases in social media to transportation activities. In an embodiment, the neural network 18108 predicts at least one destination and time of arrival for a subset 18110 of several individuals who share common transportation needs. In an embodiment, the neural network 18108 predicts common transportation needs based on an analysis of transportation need suggestive keywords detected in discussion threads among some of the individuals in a social group. In an embodiment, the method further includes identifying at least one shared transportation service 18111 that facilitates a portion of the social group meeting predicted common transportation needs 18112. In an embodiment, the at least one shared transportation service comprises generating a vehicle route that facilitates picking up a portion of the social group.

[0457] Figure 20 shows a method 2000 for predicting group transportation needs according to embodiments of the systems and methods disclosed herein. In 2002, the method includes collecting social media source data relating to multiple individuals, the data being supplied from multiple social media sources. In 2004, the method includes processing the data to identify a subset of multiple individuals who share a need for group transportation. In 2006, the method includes detecting keywords in the data that indicate a need for group transportation for the subset of multiple individuals. In 2008, the method includes predicting group transportation needs using a neural network trained to predict transportation needs based on the detected keywords. In 2009, the method includes instructing a vehicle routing system to meet the need for group transportation.

[0458] Referring to Figures 18 and 20, in an embodiment, the neural network 18108 is a convolutional neural network 18113. In an embodiment, directing a vehicle routing system to meet group transport needs includes routing multiple vehicles to destinations derived from social media-source data 18114. In an embodiment, the neural network 18108 is trained on a model that facilitates matching phrases in social media-source data 18114 with transport activities. In an embodiment, the method further includes the neural network 18108 predicting at least one destination and time of arrival for a subset 18110 of multiple individuals 18109 who share group transport needs. In an embodiment, the method further includes the neural network 18108 predicting group transport needs based on an analysis of transport need suggestive keywords detected in discussion threads within the social media-source data 18114. In an embodiment, the method further includes identifying at least one shared transport service 18111 that facilitates meeting the predicted group transport needs for at least a portion of the subset 18110 of multiple individuals. In one embodiment, at least one shared transport service 18111 comprises generating a vehicle route that facilitates picking up at least a portion of a subset 18110 of several individuals.

[0459] Figure 21 illustrates a method 2100 for predicting the need for mass transport according to embodiments of the systems and methods disclosed herein. In 2102, the method includes collecting social media source data from multiple social media sources. In 2104, the method includes processing the data to identify an event. In 2106, the method includes detecting keywords in the data that indicate an event to determine the need for transport associated with the event. In 2106, the method includes instructing a vehicle routing system to meet the transport needs using a neural network trained to predict transport needs based at least in part on social media source data.

[0460] Referring to Figures 18 and 21, in the embodiment, the neural network 18108 is a convolutional neural network 18113. In the embodiment, the vehicle routing system is directed to meet transportation needs by routing multiple vehicles to locations associated with an event. In the embodiment, the vehicle route control system is directed to meet transportation needs by routing multiple vehicles to avoid areas adjacent to locations associated with an event. In the embodiment, the vehicle route control system is directed to meet transportation needs by routing vehicles associated with users whose social media source data 18114 does not indicate a need for transportation to avoid areas adjacent to locations associated with an event. In the embodiment, the method further includes presenting at least one transportation service to meet transportation needs. In the embodiment, the neural network 18108 is trained on a model that facilitates matching phrases in social media sourced data 18114 with transportation activities.

[0461] In an embodiment, the neural network 18108 predicts at least one of the destination and arrival time of individuals participating in an event. In an embodiment, the neural network 18108 predicts transportation needs based on an analysis of transportation need suggestive keywords detected in discussion threads within the social media source data 18114. In an embodiment, the method further comprises identifying at least one shared transportation service that facilitates meeting the predicted transportation needs of at least a subset of individuals identified in the social media source data 18114. In an embodiment, the at least one shared transportation service includes generating a vehicle route that facilitates picking up a subset of individuals identified in the social media source data 18114.

[0462] Referring to Figure 22, an embodiment provided herein includes a traffic system 22111 having a data processing system 2211 for ingesting social media data 22114 from a plurality of social data sources 2269 of 22107, and a system 2247 for processing the social data sources 22107 using a hybrid neural network 2247 and optimizing the operating state of the traffic system 22111. The hybrid neural network 2247 includes, for example, a neural network component that performs classification or prediction based on the processing of social media data 22114 (e.g., predicting high attendance rates for an event by processing images on many social media feeds that show interest in an event from many people, predicting traffic volume, etc., classifying individual interest in a topic, and many others), and other components that optimize the operating state of the transport system, such as in-vehicle state, routing state (for individual vehicles 2210 or a collection of vehicles 2294), user experience state, or other states described through this disclosure (e.g., routing individuals early to venues such as music festivals that are likely to have very high attendance, playing music content in vehicle 2210 for bands participating in the music festival, etc.).

[0463] Embodiments provided herein include a system for transport, comprising a data processing system 2211 for taking social media data 22114 from a plurality of social data sources 2269, and using a hybrid neural network 2247 to optimize the operating state of the transport system based on processing the data 22114 from the plurality of social data sources 2269 with the hybrid neural network 2247.

[0464] Embodiments provided herein include a hybrid neural network system 22115 for optimizing a transport system, the hybrid neural network system 22115 comprising a hybrid network 2247, which includes a first neural network 2222 that predicts local effects 22116 on the transport system through analysis of social media data 22114 supplied from a plurality of 2269 social media data sources 22107, and a second neural network 2220 that optimizes the operating state of the transport system based on the predicted local effects 22116.

[0465] In an embodiment, at least one of the first neural network 2222 and the second neural network 2220 is a convolutional neural network. In an embodiment, the second neural network 2220 is to optimize the in-vehicle lidar experience state. In an embodiment, the first neural network 2222 identifies a set of vehicles 2294 that contribute to a local effect 22116 based on the correlation between vehicle location and the region of the local effect 22116. In an embodiment, the second neural network 2220 is to optimize the routing state of the transport system for vehicles adjacent to the location of the local effect 22116. In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization based on keywords in social media data that indicate the results of transport system optimization actions. In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization based on social media posts.

[0466] In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on social media feeds. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on ratings derived from social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on like / dislike activity detected in social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on relational metrics in social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on user behavior detected in social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on discussion threads in social media data 22114.

[0467] In an embodiment, 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 an embodiment, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on photos in the social media data 22114. In an embodiment, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on traffic impact information in the social media data 22114. In an embodiment, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on the appearance of a particular individual at a location in the social media data 22114. In an embodiment, the particular individual is a celebrity. In an embodiment, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on the presence of rare or transient phenomena at a location in the social media data 22114.

[0468] In an embodiment, the hybrid neural network 2247 is trained to predict and optimize commerce-related events at locations within the social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize entertainment events at locations within the social media data 22114. In an embodiment, the social media data analyzed to predict local impacts on a traffic system includes traffic conditions. In an embodiment, the social media data analyzed to predict local impacts on a traffic system includes weather conditions. In an embodiment, the social media data analyzed to predict local impacts on a traffic system includes entertainment options.

[0469] In embodiments, social media data analyzed to predict local impacts on a transportation system includes risk-related conditions. In embodiments, risk-related conditions include crowds gathering for potentially dangerous reasons. In embodiments, social media data analyzed to predict local impacts on a transportation system includes commercial-related conditions. In embodiments, social media data analyzed to predict local impacts on a transportation system includes objective-related conditions.

[0470] In an embodiment, the social media data analyzed to predict local impacts on the transportation system includes estimates of event attendees. In an embodiment, the social media data analyzed to predict local impacts on the transportation system includes predictions of event attendees. In an embodiment, the social media data analyzed to predict local impacts on the transportation system includes modes of transport. In an embodiment, modes of transport include automobile traffic. In an embodiment, modes of transport include public transport options.

[0471] In an embodiment, the social media data analyzed to predict local impacts on the transportation system includes hashtags. In an embodiment, the social media data analyzed to predict local impacts on the transportation system includes topic trending. In an embodiment, the outcome of the transportation system optimization action is a reduction in fuel consumption. In an embodiment, the outcome of the transportation system optimization action is a reduction in traffic congestion. In an embodiment, the outcome of the transportation system optimization action is a reduction in pollution. In an embodiment, the outcome of the transportation system optimization action is the avoidance of adverse weather conditions. In an embodiment, the operating state of the transportation system to be optimized includes the in-vehicle state. In an embodiment, the operating state of the transportation system to be optimized includes the routing state.

[0472] In one embodiment, the routing state is for an individual vehicle 2210. In another embodiment, the routing state is for a set of vehicles 2294. In yet another embodiment, the operating state of the optimized transport system includes the user experience state.

[0473] Figure 23 shows a method 2300 for optimizing the operational state of a transport system according to embodiments of the systems and methods disclosed herein. In 2302, the method includes collecting social media source data relating to multiple individuals, the data being supplied from multiple social media sources. In 2306, the method includes predicting the impact on the transport system through analysis of data supplied from social media by a first neural network of the hybrid neural network. In 2308, the method includes optimizing at least one operational state of the transport system in response to the predicted impact by a second neural network of the hybrid neural network.

[0474] Referring to Figures 22 and 23, in the embodiment, at least one of the first neural network 2222 and the second neural network 2220 is a convolutional neural network. In the embodiment, the second neural network 2220 optimizes the in-vehicle LiDAR experience state. In the embodiment, the first neural network 2222 identifies a set of vehicles contributing to the effect based on the correlation between vehicle position and the area of ​​effect. In the embodiment, the second neural network 2220 optimizes the routing state of the traffic system for vehicles close to the location of the effect.

[0475] In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on keywords in social media data that indicate the results of traffic system optimization actions. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on social media posts. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on social media feeds. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on ratings derived from social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on like / dislike activity detected in social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on relational indicators in social media data 22114.

[0476] In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on user behavior detected in social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on discussion threads in social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on chats in social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on photos in social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on information influencing traffic in social media data 22114.

[0477] In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization based on the appearance of a specific individual at a location in social media data. In an embodiment, the specific individual is a celebrity. In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization based on the presence of rare or transient phenomena at a location in social media data. In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization of commerce-related events at a location in social media data. In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization of entertainment events at a location in social media data. In an embodiment, the social media data analyzed to predict the impact on a traffic system includes traffic conditions.

[0478] In embodiments, the social media data analyzed to predict the impact on the transportation system includes weather conditions. In embodiments, the social media data analyzed to predict the impact on the transportation system includes entertainment options. In embodiments, the social media data analyzed to predict the impact on the transportation system includes risk-related conditions. In embodiments, risk-related conditions include crowds gathering for potentially dangerous reasons. In embodiments, the social media data analyzed to predict the impact on the transportation system includes commercial conditions. In embodiments, the social media data analyzed to predict the impact on the transportation system includes goal-related conditions.

[0479] In an embodiment, the social media data analyzed to predict the impact on the transportation system includes estimates of event attendees. In an embodiment, the social media data analyzed to predict the impact on the transportation system includes predictions of event attendees. In an embodiment, the social media data analyzed to predict the impact on the transportation system includes modes of transport. In an embodiment, modes of transport include automobile traffic. In an embodiment, modes of transport include public transport options. In an embodiment, the social media data analyzed to predict the impact on the transportation system includes hashtags. In an embodiment, the social media data analyzed to predict the impact on the transportation system includes topic trending.

[0480] In an embodiment, the outcome of the traffic system optimization action is a reduction in fuel consumption. In an embodiment, the outcome of the traffic system optimization action is a reduction in traffic congestion. In an embodiment, the outcome of the traffic system optimization action is a reduction in pollution. In an embodiment, the outcome of the traffic system optimization action is the avoidance of bad weather. In an embodiment, the operating state of the traffic system to be optimized includes the in-vehicle state. In an embodiment, the operating state of the transportation system to be optimized includes the routing state. In an embodiment, the routing state is for individual vehicles. In an embodiment, the routing state is for a set of vehicles. In an embodiment, the operating state of the transportation system to be optimized includes the user experience state.

[0481] Figure 24 shows a method 2400 for optimizing the operating state of a transport system according to embodiments of the systems and methods disclosed herein. In 2402, the method includes classifying social media data supplied from multiple social media sources as affecting the transport system using a first neural network of a hybrid neural network. In 2404, the method includes predicting at least one operating objective of the transport system based on the classified social media data using a second network of a hybrid neural network. In 2406, the method includes optimizing the operating state of the transport system to achieve at least one operating objective of the transport system using a third network of a hybrid neural network.

[0482] Referring to Figures 22 and 24, in this embodiment, at least one of the neural networks in the hybrid neural network 2247 is a convolutional neural network.

[0483] Referring to Figure 25, embodiments provided herein include a transport system 2511 having a data processing system 2562 for taking social media data 25114 from multiple social data sources 25107, and a hybrid neural network 2547 can be used to optimize the operating state 2545 of a vehicle 2510 based on processing the social data sources using the hybrid neural network 2547. In embodiments, the hybrid neural network 2547 may include one neural network category for prediction, another for classification, and another for optimizing one or more operating states, such as providing one or more desired outcomes (e.g., efficient travel, a satisfying rider experience, a comfortable ride, on-time arrival). Social data sources 2569 may be used by different neural network categories (such as any of the types described herein) for predicting travel time, classifying content such as profiling user interests, or predicting the purpose of a transport plan (e.g., providing overall satisfaction for an individual or group). Social data sources 2569 can also inform optimization by providing indicators of successful outcomes (for example, a social data source 25107 like a Facebook feed might indicate that a trip was "great" or "terrible," a Yelp review might indicate that a restaurant was terrible, etc.). Thus, social data sources 2569 can be used to train the system to optimize travel planning by contributing to outcome tracking, such as timing, destination, purpose of travel, which individuals to invite, what entertainment options to choose, and many other things.

[0484] Embodiments provided herein include a transport system 2511 comprising a data processing system 2562 for taking in social media data 25114 from a plurality of social data sources 25107, and a hybrid neural network 2546 for optimizing the operating state 2545 of a vehicle 2510 based on processing the data 25114 from the plurality of social data sources 25107 with a hybrid neural network 2547.

[0485] Figure 26 shows a method 2600 for optimizing the operating state of a vehicle according to embodiments of the systems and methods disclosed herein. In 2602, the method includes classifying social media data 25119 (Figure 25) supplied from multiple social media sources as affecting a transport system, using a first neural network 2522 (Figure 25) of a hybrid neural network. In 2604, the method includes predicting one or more effects 25118 (Figure 25) of the classified social media data on the transport system, using a second neural network 2520 (Figure 25) of a hybrid neural network. In 2606, the method includes optimizing the state of at least one vehicle in the transport system using a third neural network 25117 (Figure 25) of a hybrid neural network, the optimization including addressing the effects of one or more predicted effects on at least one vehicle.

[0486] Referring to Figures 25 and 26, in an embodiment, at least one of the neural networks in the hybrid neural network 2547 is a convolutional neural network. In an embodiment, social media data 25114 includes social media posts. In an embodiment, social media data 25114 includes social media feeds. In an embodiment, social media data 25114 includes like / dislike activity detected on social media. In an embodiment, social media data 25114 includes relational metrics. In an embodiment, social media data 25114 includes user behavior. In an embodiment, social media data 25114 includes discussion threads. In an embodiment, social media data 25114 includes chats. In an embodiment, social media data 25114 includes photos.

[0487] In embodiments, social media data 25114 includes traffic affection information. In embodiments, social media data 25114 includes the appearance of a specific individual at a location. In embodiments, social media data 25114 includes the appearance of a celebrity at a location. In embodiments, social media data 25114 includes the presence of rare or transient phenomena at a location. In embodiments, social media data 25114 includes commerce-related events. In embodiments, social media data 25114 includes entertainment events at a location. In embodiments, social media data 25114 includes traffic conditions. In embodiments, social media data 25114 includes weather conditions. In embodiments, social media data 25114 includes entertainment options.

[0488] In embodiments, social media data 25114 includes risk-related conditions. In embodiments, social media data 25114 includes predictions of event attendance. In embodiments, social media data 25114 includes estimates of event attendance. In embodiments, social media data 25114 includes means of transportation used in conjunction with the event. In embodiments, the effect on transportation 25118 includes a reduction in fuel consumption. In embodiments, the effect on transportation systems 25118 includes a reduction in traffic congestion. In embodiments, the effect on transportation systems 25118 includes a reduction in carbon footprint. In embodiments, the effect on transport systems 25118 includes reduced pollution.

[0489] In an embodiment, the optimized state 2544 of at least one vehicle 2510 is the vehicle's operating state 2545. In an embodiment, the optimized state of at least one vehicle includes an in-vehicle state. In an embodiment, the optimized state of at least one vehicle includes a lidar state. In an embodiment, the optimized state of at least one vehicle includes a routing state. In an embodiment, the optimized state of at least one vehicle includes a user experience state. In an embodiment, characterization of the optimization results in social media data 25114 is used as feedback to improve the optimization. In an embodiment, the feedback includes likes and dislikes of the results. In an embodiment, the feedback includes social media activities that refer to the results.

[0490] In embodiments, feedback includes trending social media activities that reference the results. In embodiments, feedback includes hashtags related to the results. In embodiments, feedback includes evaluation of the results. In embodiments, feedback includes requests for the results.

[0491] Figure 26A shows a method 26A00 for optimizing the operating state of a vehicle according to embodiments of the systems and methods disclosed herein. In 26A02, the method includes classifying social media data supplied from multiple social media sources as affecting a transport system, using a first neural network of a hybrid neural network. In 26A04, the method includes predicting at least one vehicle operating objective for the transport system based on the classified social media data, using a second neural network of a hybrid neural network. In 26A06, the method includes optimizing the state of a vehicle in a transport system to achieve at least one vehicle operating objective for the transport system, using a third neural network of a hybrid neural network.

[0492] Referring to Figures 25 and 26A, in this embodiment, at least one of the neural networks in the hybrid neural network 2547 is a convolutional neural network. In this embodiment, the vehicle operation objective is to achieve the rider state of at least one rider in the vehicle. In this embodiment, the social media data 25114 includes social media posts.

[0493] In an embodiment, social media data 25114 includes social media feeds. In an embodiment, social media data 25114 includes like / dislike activity detected on social media. In an embodiment, social media data 25114 includes relational indicators. In an embodiment, social media data 25114 includes user behavior. In an embodiment, social media data 25114 includes discussion threads. In an embodiment, social media data 25114 includes chats. In an embodiment, social media data 25114 includes photos. In an embodiment, social media data 25114 includes traffic affection information.

[0494] In embodiments, social media data 25114 includes the presence of a specific individual at a location. In embodiments, social media data 25114 includes the presence of a celebrity at a location. In embodiments, social media data 25114 includes the presence of a rare or transient phenomenon at a location. In embodiments, social media data 25114 includes commerce-related events. In embodiments, social media data 25114 includes entertainment events at a location. In embodiments, social media data 25114 includes traffic conditions. In embodiments, social media data 25114 includes weather conditions. In embodiments, social media data 25114 includes entertainment options.

[0495] In embodiments, social media data 25114 includes risk-related conditions. In embodiments, social media data 25114 includes predictions of event attendance. In embodiments, social media data 25114 includes estimations of event attendance. In embodiments, social media data 25114 includes modes of transportation used with the event. In embodiments, effects on transportation include reduced fuel consumption. In embodiments, effects on the transportation system include reduced traffic congestion. In embodiments, effects on the transportation system include a reduction in carbon footprint. In embodiments, effects on the transport system include reduced pollution. In embodiments, the optimized state of the vehicle is the operating state of the vehicle.

[0496] In embodiments, the optimized state of the vehicle includes the in-vehicle state. In embodiments, the optimized state of the vehicle includes the lidar state. In embodiments, the optimized state of the vehicle includes the routing state. In embodiments, the optimized state of the vehicle includes the user experience state. In embodiments, characterization of the optimization results in social media data is used as feedback to improve the optimization. In embodiments, the feedback includes likes and dislikes of the results. In embodiments, the feedback includes social media activities referencing the results. In embodiments, the feedback includes trending social media activities referencing the results.

[0497] In an embodiment, the feedback includes hashtags associated with the results. In an embodiment, the feedback includes evaluations of the results. In an embodiment, the feedback includes requests for the results.

[0498] Referring to Figure 27, embodiments provided herein include a transport system 2711 having a data processing system 2762 for taking social data 27114 from a plurality of social data sources 2769, and using a hybrid neural network 2746 to optimize the satisfaction 27121 of at least one rider 27120 in a vehicle 2710 based on processing the social data sources using a hybrid neural network 2747. The social data sources 2769 may be used, for example, by one neural network category to predict which entertainment options are most effective for the rider 27120, and another neural network category may be used to optimize route planning (for example, based on social data indicating likely traffic, points of interest, etc.). The social data 27114 may also be used for result tracking and feedback to optimize the system with respect to entertainment options, traffic planning, routing, etc.

[0499] Embodiments provided herein include a transport system 2711 comprising a data processing system 2762 for taking in social data 27114 from a plurality of social data sources 2769, and a hybrid neural network 2746 for optimizing the satisfaction level 27121 of at least one rider 27120 in a vehicle 2710 based on processing the social data 27114 from the plurality of social data sources 2769 with a hybrid neural network 2747.

[0500] Figure 28 shows a method 2800 for optimizing rider satisfaction according to embodiments of the systems and methods disclosed herein. In 2802, the method includes classifying social media data 27119 (Figure 27) supplied from multiple social media sources as indicating an impact on a transport system, using a first neural network 2722 (Figure 27) of a hybrid neural network. In 2804, the method includes predicting at least one aspect 27122 (Figure 27) of rider satisfaction that is affected by an impact on a transport system, derived from the social media data classified as indicating an impact on a transport system, using a second neural network 2720 (Figure 27) of a hybrid neural network. In 2806, the method includes optimizing at least one aspect of rider satisfaction for at least one rider occupying a vehicle in a transport system, using a third neural network 27117 (Figure 27) of a hybrid neural network.

[0501] Referring to Figures 27 and 28, in an embodiment, at least one of the neural networks in the hybrid neural network 2547 is a convolutional neural network. In an embodiment, at least one aspect 27121 of rider satisfaction is optimized by predicting entertainment options to present to the rider. In an embodiment, at least one aspect 27121 of rider satisfaction is optimized by optimizing route planning for vehicles occupied by the rider. In an embodiment, at least one aspect 27121 of rider satisfaction is the rider state, and optimization of an aspect of rider satisfaction is performed, which includes optimizing the rider state. In an embodiment, social media data specific to the rider is analyzed to determine at least one optimization action that is likely to optimize at least one aspect 27121 of rider satisfaction. In an embodiment, the optimization action is selected from a group of actions consisting of adjusting route planning to include passing through points of interest to the user, avoiding traffic congestion predicted from social media data, and presenting entertainment options.

[0502] In the embodiment, social media data includes social media posts. In the embodiment, social media data includes social media feeds. In the embodiment, social media data includes like / dislike activity detected on social media. In the embodiment, social media data includes relationship indicators. In the embodiment, social media data includes user behavior. In the embodiment, social media data includes discussion threads. In the embodiment, social media data includes chats. In the embodiment, social media data includes photos.

[0503] In embodiments, social media data includes information that affects traffic. In embodiments, social media data includes the presence of a specific individual in a particular location. In embodiments, social media data includes the presence of a celebrity in a particular location. In embodiments, social media data includes the presence of a rare or transient phenomenon in a particular location. In embodiments, social media data includes commerce-related events. In embodiments, social media data includes entertainment events in a particular location. In embodiments, social media data includes traffic conditions. In embodiments, social media data includes weather conditions. In embodiments, social media data includes entertainment options. In embodiments, social media data includes risk-related conditions. In embodiments, social media data includes predictions of event attendance. In embodiments, social media data includes estimates of event attendance. In embodiments, social media data includes modes of transportation used in conjunction with the event. In embodiments, the effect on transportation 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 reducing the carbon footprint. In embodiments, the effect on the transport system includes reducing pollution. In embodiments, at least one optimized aspect of rider satisfaction is the operating state of the vehicle. In an embodiment, at least one optimized aspect of rider satisfaction includes the in-vehicle state. In an embodiment, at least one optimized aspect of rider satisfaction includes the rider state. In an embodiment, at least one optimized aspect of rider satisfaction includes the routing state. In an embodiment, at least one optimized aspect of rider satisfaction includes the user experience state.

[0504] In embodiments, characterization of optimization results in social media data is used as feedback to improve the optimization. In embodiments, the feedback includes likes and dislikes of the results. In embodiments, the feedback includes social media activities referencing the results. In embodiments, the feedback includes trending social media activities referencing the results. In embodiments, the feedback includes hashtags related to the results. In embodiments, the feedback includes evaluations of the results. In embodiments, the feedback includes requests for the results.

[0505] Embodiments provided herein include a rider satisfaction system 27123 for optimizing rider satisfaction 27121, the system comprising: a first neural network 2722 of a hybrid neural network 2747 for classifying social media data 27114 supplied from a plurality of social media sources 27107 as indicating an impact on a traffic system 2711; a second neural network 2720 of the hybrid neural network 2747 for predicting at least one aspect 27122 of rider satisfaction 27121 that is affected by the impact on the traffic system, obtained from the social media data classified as indicating an impact on the traffic system; and a third neural network 27117 of the hybrid neural network 2747 for optimizing at least one aspect 27121 of rider satisfaction for at least one rider 2744 occupying a vehicle 2710 in the traffic system 2711. In embodiments, at least one of the neural networks of the hybrid neural network 2747 is a convolutional neural network.

[0506] In an embodiment, at least one aspect of rider satisfaction 27121 is optimized by predicting entertainment options to present to rider 2744. In an embodiment, at least one aspect of rider satisfaction 27121 is optimized by optimizing route planning for vehicles 2710 occupied by rider 2744. In an embodiment, at least one aspect of rider satisfaction 27121 is rider state 2737, and optimizing at least one aspect of rider satisfaction 27121 consists of optimizing rider state 2737. In an embodiment, social media data specific to rider 2744 is analyzed to determine at least one optimization action that is likely to optimize at least one aspect of rider satisfaction 27121. In an embodiment, at least one optimization action is selected from the group consisting of adjusting route planning to include waypoints of interest to the user, avoiding traffic congestion predicted from social media data, obtaining economic benefits, obtaining altruistic benefits, and presenting entertainment options.

[0507] In an embodiment, the economic benefit is fuel saved. In an embodiment, the altruistic benefit is a reduction in environmental impact. In an embodiment, social media data includes social media posts. In an embodiment, social media data includes social media feeds. In an embodiment, social media data includes like / dislike activity detected on social media. In an embodiment, social media data includes relational indicators. In an embodiment, social media data includes user behavior. In an embodiment, social media data includes discussion threads. In an embodiment, social media data includes chats. In an embodiment, social media data includes photos. In an embodiment, social media data includes information that influences traffic. In an embodiment, social media data includes the display of a specific individual in a particular location.

[0508] In an embodiment, social media data includes the appearance of a celebrity at a location. In an embodiment, social media data includes the presence of a rare or transient phenomenon at a location. In an embodiment, social media data includes commerce-related events. In an embodiment, social media data includes entertainment events at a location. In an embodiment, social media data includes traffic conditions. In an embodiment, social media data includes weather conditions. In an embodiment, social media data includes entertainment options. In an embodiment, social media data includes risk-related conditions. In an embodiment, social media data includes predictions of event attendance. In an embodiment, social media data includes estimates of event attendance. In an embodiment, social media data includes means of transportation used in conjunction with the event.

[0509] In an embodiment, the effect on the traffic system includes reducing fuel consumption. In an embodiment, the effect on the traffic system includes reducing traffic congestion. In an embodiment, the effect on the transport system includes reducing the carbon footprint. In an embodiment, the effect on the transport system includes reducing pollution. In an embodiment, at least one optimized aspect of rider satisfaction is the operating state of the vehicle. In an embodiment, at least one optimized aspect of rider satisfaction includes the in-vehicle state. In an embodiment, at least one optimized aspect of rider satisfaction includes the rider state. In an embodiment, at least one optimized aspect of rider satisfaction includes the routing state. In an embodiment, at least one optimized aspect of rider satisfaction includes the user experience state. In an embodiment, characterization of the optimization results in social media data is used as feedback to improve the optimization. In an embodiment, the feedback includes likes and dislikes of the results. In an embodiment, the feedback includes social media activities referencing the results. In an embodiment, the feedback includes trending social media activities referencing the results. In an embodiment, the feedback includes hashtags related to the results. In an embodiment, the feedback includes evaluations of the results. In an embodiment, the feedback includes requests for the results.

[0510] Referring to Figure 29, an embodiment provided herein is a transport system 2911 having a hybrid neural network 2947, in which one neural network 2922 processes sensor inputs 29125 relating to the lidar 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 emotional state 2966. For example, a neural network 2922, including one or more perceptrons 29127 that mimic human sensations, is used to mimic or assist in determining the likely emotional states of the lidar 29126 based on the degree to which various sensations have been stimulated, and another neural network 2920 is used for an expert system that performs random and / or systematized fluctuations of various combinations of operating parameters (such as entertainment settings, seat settings, suspension settings, route type, etc.), optionally using genetic programming to promote preferred combinations and eliminate unfavorable combinations based on inputs from the output of the perceptron-containing neural network 2922 that predicts emotional states. These and many other such combinations are covered by this disclosure. In Figure 29, the perceptron 29127 is depicted as optional.

[0511] Embodiments provided herein are a transport system 2911 characterized in that one neural network 2922 processes sensor inputs 29125 corresponding to a lidar 2944 of a vehicle 2910 to determine the emotional state 2966 of the lidar 2944, and another neural network 2920 comprises a hybrid neural network 2947 that optimizes at least one operating parameter 29124 of the vehicle to improve the emotional state 2966 of the lidar 2944.

[0512] Embodiments provided herein are a hybrid neural network 2947 for rider satisfaction, comprising a rider emotional state detection device 2920, which includes a first neural network 2922 for detecting a detected emotional state 29126 of a rider 2944 occupying a vehicle 2910 through analysis of sensor inputs 29125 collected from sensors 2925 deployed in a vehicle 2910 to collect the physiological state of the rider, and a second neural network 2920 for optimizing vehicle operating parameters 29124 according to the rider's preferred emotional state 29126.

[0513] In an embodiment, the first neural network 2922 is a recurrent neural network, and the second neural network 2920 is a radial basis function neural network. In an embodiment, at least one of the neural networks in the hybrid neural network 2947 is a convolutional neural network. In an embodiment, the second neural network 2920 optimizes the operation parameters 29124 based on the correlation between the vehicle operating state 2945 and the ridor's emotional state 2966. In an embodiment, the second neural network 2920 optimizes the operation parameters 29124 in real time in response to the detection of the detected emotional state 29126 of the ridor 2944 by the first neural network 2922. In an embodiment, the first neural network 2922 consists of a plurality of connected nodes that form a directed cycle, and the first neural network 2922 further facilitates the bidirectional flow of data between the connected nodes. In the embodiment, the optimized operational parameter 29124 affects at least one of the following: the vehicle's path, the in-vehicle audio content, the vehicle's speed, the vehicle's acceleration, the vehicle's deceleration, the proximity to objects along the path, and the proximity to other vehicles along the path.

[0514] Embodiments provided herein are artificial intelligence systems 2936 for optimizing rider satisfaction, comprising a hybrid neural network 2947: a recurrent neural network (for example, as follows; in Figure 29, the neural network 2922 may be a recurrent neural network), which optimizes vehicle driving parameters 29124 in response to instructions for changes in the rider's emotional state in order to achieve a preferred emotional state for the rider, using a neural network for indicating changes in the rider's emotional state within the vehicle 2910 through recognition of patterns in the rider's physiological data captured by at least one sensor 2925 deployed to capture data indicating the emotional state of the rider while in the vehicle 2910, and a radial basis function neural network (for example, in Figure 29, the second neural network 2920 may be a radial basis function neural network). In embodiments, the vehicle driving parameters 29124 to be optimized are determined and adjusted to induce a preferred emotional state for the rider.

[0515] Embodiments provided herein are artificial intelligence systems 2936 for optimizing rider satisfaction, comprising: a hybrid neural network 2947 including: a convolutional neural network for indicating changes in the emotional state of a rider in a vehicle through recognition of patterns in the rider's visual data captured by at least one image sensor (in Figure 29, the neural network 1 depicted by reference numeral 2922 may optionally be a convolutional neural network); a second neural network 2920 for indicating changes in the emotional state of a rider in a vehicle through recognition of patterns in the rider's visual data captured by 29 (in Figure 29, the network 2 depicted by reference numeral 2922 may optionally not be a neural network); and a second neural network 2920 for optimizing vehicle driving parameters 29124 in response to instructions for changes in the rider's emotional state in order to achieve a preferred emotional state for the rider.

[0516] In one embodiment, the vehicle operation parameters 19124 to be optimized are determined and adjusted to induce a desirable emotional state in the rider.

[0517] Referring to Figure 30, an embodiment provided herein provides a transport system 3011 having an artificial intelligence system 3036 for processing feature vectors of an image of a rider's face in a vehicle to determine an emotional state and for optimizing at least one operating parameter of the vehicle to improve the rider's emotional state. Faces can be classified based on images from an in-vehicle camera, a camera of an available mobile phone or other mobile device, or other sources. An expert system, optionally trained on a training set of data provided by a human or trained by deep learning, may learn to adjust vehicle parameters (such as any described herein) to provide an improved emotional state. For example, if the rider's face indicates stress, the vehicle may select a less stressful route, play relaxing music, play humorous content, etc.

[0518] Embodiments provided herein are a transport system 3011 comprising: an artificial intelligence system 3036 for processing feature vectors of an image 30129 of a face 30128 of a lidar 3044 in a vehicle 3010 to determine the lidar's emotional state 3066; and an artificial intelligence system for optimizing vehicle operation parameters 30124 to improve the emotional state 3066 of the lidar 3044.

[0519] In this embodiment, the artificial intelligence system 3036 includes: a first neural network 3022 that detects the rider's emotional state 30126 through the recognition of a pattern of feature vectors 30130 of an image 30129 of the rider's face 30128 in a vehicle 3010, wherein the feature vectors 30130 indicate at least one of the rider's favorable emotional state and the rider's unfavorable emotional state; and a second neural network 3020 that optimizes the vehicle's operating parameters 30124 in response to the detected rider's emotional state 30126 in order to achieve the rider's favorable emotional state.

[0520] In an embodiment, the first neural network 3022 is a recurrent neural network, and the second neural network 3020 is a radial basis function neural network. In an embodiment, the second neural network 3020 optimizes the operation parameters 30124 based on the correlation between the vehicle operating state 3045 and the rider's emotional state 3066. In an embodiment, the second neural network 3020 determines the optimal values ​​of the vehicle driving parameters, and the transport system 3011 adjusts the vehicle driving parameters 30124 to the optimal values ​​to induce a favorable emotional state for the rider. In an embodiment, the first neural network 3022 further learns to classify feature vector patterns by processing the training dataset 30131 and to associate the patterns with a set of emotional states and their changes. In one embodiment, the training dataset 30131 is supplied from at least one of the following data streams: an unstructured data source, a social media source, a wearable device, an in-vehicle sensor, a rider helmet, rider headgear, and a rider voice recognition system.

[0521] In an embodiment, the second neural network 3020 optimizes the operation parameters 30124 in real time in response to the detection of the lidar's emotional state by the first neural network 3022. In an embodiment, the first neural network 3022 detects a pattern in the feature vectors. In an embodiment, the pattern is associated with the change in the lidar's emotional state from a first emotional state to a second emotional state. In an embodiment, the second neural network 3020 optimizes the vehicle's driving parameters in response to the detection of the pattern associated with the change in emotional state. In an embodiment, the first neural network 3022 comprises a plurality of interconnected nodes that form a directed cycle, and the first neural network 3022 further facilitates the bidirectional flow of data between the interconnected nodes. In one embodiment, the transport system 3011 is a feature vector generation system for processing a set of images of a lidar's face, wherein the processing of the set of images is to generate a feature vector 30130 of the lidar's face images, where the set of images is captured over time from a plurality of image capture devices 3027 while the lidar 3044 is in the vehicle 3010. In another embodiment, the transport system further comprises an image capture device 3027 arranged to capture a set of images of a lidar's face inside the vehicle from a plurality of viewpoints, and an image processing system that generates a feature vector from a set of images captured from at least one of the plurality of viewpoints.

[0522] In an embodiment, the transport system 3011 further comprises an interface 30133 between a first neural network and an image processing system 30132 for communicating a time sequence of feature vectors, wherein the feature vectors represent the emotional state of the lidar. In an embodiment, the feature vectors represent at least one of the following: a change in the lidar's emotional state, a stable emotional state of the lidar, a rate of change in the lidar's emotional state, a direction of change in the lidar's emotional state, a polarity of change in the lidar's emotional state, a change in the lidar's emotional state to an undesirable emotional state, and a change in the lidar's emotional state to a desirable emotional state.

[0523] In embodiments, the operational parameters to be optimized affect at least one of the following: the vehicle's path, in-vehicle audio content, vehicle speed, vehicle acceleration, vehicle deceleration, proximity to objects along the path, and proximity to other vehicles along the path. In embodiments, a second neural network interacts with the vehicle control system to adjust the operational parameters. In embodiments, the artificial intelligence system further comprises a neural network including one or more perceptrons that mimic human sensations to facilitate determining the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated. In embodiments, the artificial intelligence system includes a recurrent neural network that directs changes in the rider's emotional state through the recognition of a pattern of feature vectors of an image of the rider's face in the vehicle, and a radial basis function neural network that optimizes the vehicle's operational parameters to achieve a preferred emotional state for the rider in response to the directives for changes in the rider's emotional state.

[0524] In an embodiment, the radial basis function neural network optimizes operational parameters based on the correlation between the vehicle's operating state and the rider's emotional state. In an embodiment, the vehicle's driving parameters to be optimized are determined and adjusted to induce a preferred rider emotional state. In an embodiment, the recurrent neural network further learns to associate the feature vector patterns with emotional states and their changes by classifying feature vector patterns from a training dataset supplied from at least one of the following data streams: unstructured data sources, social media sources, wearable devices, in-vehicle sensors, rider helmets, rider headgear, and rider voice systems. In an embodiment, the radial basis function neural network optimizes operational parameters in real time in response to the recurrent neural network's detection of changes in the rider's emotional state. In an embodiment, the recurrent neural network detects feature vector patterns indicating that the rider's emotional state is changing from a first emotional state to a second emotional state. In an embodiment, the radial basis function neural network optimizes the vehicle's operating parameters in response to the indicated change in emotional state.

[0525] In an embodiment, the recurrent neural network comprises multiple connected nodes that form a directed cycle, and the recurrent neural network further facilitates the bidirectional flow of data between the connected nodes. In an embodiment, the feature vector indicates that the lidar's emotional state is changing, the lidar's emotional state is stable, the rate of change in the lidar's emotional state, the direction of change in the lidar's emotional state, and the polarity of change in the lidar's emotional state, indicating that the lidar's emotional state is changing to an undesirable state and the lidar's emotional state is changing to a favorable state. In an embodiment, the operational parameters to be optimized affect at least one of the following: the vehicle's path, the in-vehicle audio content, the vehicle's speed, the vehicle's acceleration, the vehicle's deceleration, proximity to objects along the path, and proximity to other vehicles along the path.

[0526] In an embodiment, the radial basis function neural network interacts with the vehicle control system 30134 to adjust the operational parameters 30124. In an embodiment, the artificial intelligence system 3036 further comprises a neural network including one or more perceptrons that mimic human sensations, which facilitates determining the Rider's emotional state based on the degree to which at least one of the Rider's senses is stimulated. In an embodiment, the artificial intelligence system 3036 maintains the Rider's preferred emotional state via a modular neural network, which comprises a Rider Emotional State Determination Neural Network that processes feature vectors of an image of the Rider's face in the vehicle to detect patterns. In an embodiment, the pattern of the feature vectors indicates at least one of a preferred emotional state and an unpredictable emotional state, which comprises an intermediary circuit that translates data from the Rider Emotional State Determination Neural Network into vehicle operational state data, and a vehicle operational state optimization neural network that adjusts the vehicle's operational parameters in response to the vehicle operational state data.

[0527] In an embodiment, the vehicle operating state optimization neural network adjusts vehicle operating parameters 30124 to achieve a preferred emotional state for the rider. In an embodiment, the vehicle operating state optimization neural network optimizes operating parameters based on the correlation between vehicle operating states 3045 and rider emotional states 3066. In an embodiment, the vehicle driving parameters to be optimized are determined and adjusted to induce a preferred rider emotional state. In an embodiment, the rider emotional state determination neural network classifies feature vector patterns from a training dataset supplied from at least one of the following data streams: unstructured data sources, social media sources, wearable devices, in-vehicle sensors, rider helmets, rider headgear, and rider voice systems, and further learns to associate feature vector patterns with emotional states and their changes.

[0528] In one embodiment, the vehicle driving state optimization neural network optimizes driving parameters 30124 in real time in response to the detection of changes in the rider's emotional state 30126 by the rider emotional state determination neural network. In another embodiment, the rider emotional state determination neural network detects patterns of feature vectors 30130 indicating that the rider's emotional state is changing from a first emotional state to a second emotional state. In yet another embodiment, the vehicle driving state optimization neural network optimizes the vehicle's driving parameters in response to the indicated change in emotional state. In yet another embodiment, the artificial intelligence system 3036 consists of a plurality of connected nodes that form a directed cycle, and the artificial intelligence system further facilitates the bidirectional flow of data between the connected nodes.

[0529] In an embodiment, the feature vector 30130 indicates at least one of the following: the Rider's emotional state is changing, the Rider's emotional state is stable, the rate of change in the Rider's emotional state, the direction of change in the Rider's emotional state, and the polarity of change in the Rider's emotional state, indicating that the Rider's emotional state is changing to an undesirable state and that the Rider's emotional state is changing to a favorable state. In an embodiment, the driving parameters to be optimized affect at least one of the following: the vehicle route, in-vehicle audio content, vehicle speed, vehicle acceleration, vehicle deceleration, proximity to objects along the route, and proximity to other vehicles along the route. In an embodiment, the vehicle driving state optimization neural network interacts with the vehicle control system to adjust the driving parameters.

[0530] In an embodiment, the artificial intelligence system 3036 further includes a neural network comprising one or more perceptrons that mimic human sensations, which facilitates determining the emotional state of the rider based on the degree to which at least one of the rider's senses is stimulated. The terms “neural network” and “neural network” will be understood to be used interchangeably in this disclosure. In an embodiment, the rider emotional state determination neural network comprises one or more perceptrons that mimic human sensations, which facilitates determining the emotional state of the rider based on the degree to which at least one of the rider's senses is stimulated. In an embodiment, the artificial intelligence system 3036 includes a recurrent neural network that indicates changes in the emotional state of the rider in the vehicle through the recognition of a pattern of feature vectors of an image of the rider's face in the vehicle, and the transport system further comprises a vehicle control system 30134 that controls the operation of the vehicle by adjusting a plurality of vehicle operation parameters 30124, and a feedback loop that transmits indicated changes in the rider's emotional state between the vehicle control system 30134 and the artificial intelligence system 3036. In one embodiment, the vehicle control system adjusts at least one of a plurality of vehicle operation parameters 30124 in response to an indicated change in the rider's emotional state. In another embodiment, the vehicle control system adjusts at least one of a plurality of vehicle operation parameters based on a correlation between the vehicle operation state and the rider's emotional state.

[0531] In an embodiment, the vehicle control system adjusts at least one of a plurality of vehicle operation parameters 30124 that indicate a desirable rider emotional state. In an embodiment, the vehicle control system 30134 selects at least one of the plurality of vehicle operation parameters 30124 that indicates the production of a desirable rider emotional state. In an embodiment, the recurrent neural network further learns to classify feature vector patterns from a training dataset 30131 supplied from at least one of the data flows 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 system, and associates them with emotional states and their changes. In an embodiment, the vehicle control system 30134 adjusts at least one of the plurality of vehicle operation parameters 30124 in real time. In an embodiment, the recurrent neural network detects feature vector patterns indicating that the rider's emotional state is changing from a first emotional state to a second emotional state. In an embodiment, the vehicle driving control system adjusts the vehicle driving parameters in response to the indicated change in emotional state. In one embodiment, the recurrent neural network comprises multiple connected nodes that form a directed cycle, and the recurrent neural network further facilitates the bidirectional flow of data between the connected nodes.

[0532] In embodiments, the feature vector indicates that the rider's emotional state is changing, the rider's emotional state is stable, the rate of change in the rider's emotional state, the direction of change in the rider's emotional state, and the polarity of change in the rider's emotional state, indicating that the rider's emotional state is changing to an undesirable state or changing to a favorable state. In embodiments, at least one of a plurality of responsively tuned vehicle operation parameters affects the vehicle's route, in-vehicle audio content, vehicle speed, vehicle acceleration, vehicle deceleration, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, at least one of a plurality of responsively tuned vehicle operation parameters affects the operation of the vehicle's powertrain and suspension system. In embodiments, the radial basis function neural network interacts with the recurrent neural network via an intermediary component of an artificial intelligence system 3036 that generates vehicle control data showing the rider's emotional state response to the vehicle's current operating state. In one embodiment, the recognition of a feature vector pattern includes processing the feature vectors of the LiDAR face image captured before adjusting at least one of the multiple vehicle operation parameters, during adjusting at least one of the multiple vehicle operation parameters, and between at least two of the following points: after adjusting at least one of the multiple vehicle operation parameters.

[0533] In an embodiment, adjusting at least one of a plurality of vehicle operation parameters 30124 improves the emotional state of the rider in the vehicle. In an embodiment, adjusting at least one of a plurality of vehicle operation parameters changes the rider's emotional state from an undesirable emotional state to a favorable emotional state. In an embodiment, the change is indicated by a recurrent neural network. In an embodiment, the recurrent neural network directs a change in the rider's emotional state in response to a change in the vehicle operation parameter by determining the difference between a first set of feature vectors of the rider's face image captured before adjusting at least one of the plurality of operation parameters and a second set of feature vectors of the rider's face image captured during or after adjusting at least one of the plurality of operation parameters.

[0534] In one embodiment, a recurrent neural network detects a pattern of feature vectors indicating that the lidar's emotional state is changing from a first emotional state to a second emotional state. In another embodiment, a vehicle driving control system adjusts the vehicle's driving parameters in response to the indicated change in emotional state.

[0535] Referring to Figure 31, in an embodiment, provided herein is a transportation system having an artificial intelligence system for processing the voice of a rider in a vehicle to determine their emotional state and for optimizing at least one operating parameter of the vehicle to improve the rider's emotional state. The voice analysis module takes voice input and, using a training set of labeled data indicating the emotional state of an individual while they are speaking and / or whether others tag the data to indicate the emotional state perceived while the individual is speaking, a machine learning system (such as one of the types described herein) may be trained (using supervised learning, deep learning, etc.) to classify an individual's emotional state based on the voice. The machine learning system may improve the classification by using feedback from a large set of trials, the feedback in each instance indicating whether the system correctly assessed the individual's emotional state in the case of the speaking instance. Once trained to classify emotional states, an expert system (optionally using another machine learning system or other artificial intelligence system) may be trained to optimize various vehicle parameters pointed out throughout this disclosure to maintain or induce a more favorable state based on feedback of the results of a set of individual emotional states. For example, if, among many other indicators, an individual's voice indicates happiness, the expert system may select or recommend upbeat music to maintain that state. If the voice indicates stress, the system may recommend or provide a control signal to change the planned route to a less stressful one (e.g., with less stop-and-go traffic or a higher probability of on-time arrival). In embodiments, the system may be configured to engage in a dialogue (such as an on-screen or voice dialogue) that is set up to help obtain feedback from the user about the user's emotional state, such as by using the system's intelligent agent module to ask the rider a series of questions about whether the rider is experiencing stress and what the source of that stress is (e.g., traffic conditions, likelihood of being late, probability of on-time arrival, etc.).For example, the system might ask the rider about the sources of stress (such as traffic conditions, the possibility of delays, the behavior of other drivers, or other causes unrelated to the nature of the ride) and things that could reduce stress (such as route options, communication options (such as suggesting sending a note that there may be delays), entertainment options, and ride configuration options). The driver's responses are not only input into the expert system as an indicator of their emotional state, but may also be used to constrain efforts to optimize one or more vehicle parameters, such as by excluding configuration options unrelated to the driver's sources of stress from the set of available configurations.

[0536] Embodiments provided herein include a transport system 3111 comprising: an artificial intelligence system 3136 for processing the voice 31135 of a lidar 3144 in a vehicle 3110 to determine the emotional state 3166 of the lidar 3144; and an embodiment for optimizing at least one operating parameter 31124 of the vehicle 3110 to improve the emotional state 3166 of the lidar 3144.

[0537] Embodiments provided herein are artificial intelligence systems 3136 for voice processing to improve rider satisfaction in a traffic system 3111, comprising: a rider voice capture system 30136 positioned to capture voice output 31128 of a rider 3144 riding in a vehicle 3110; a voice analysis circuit 31132 trained using machine learning to classify the rider's emotional state 31138 based on the captured rider's voice output; and an expert system 31139 using machine learning to optimize at least one operating parameter 31124 of the vehicle to change the rider's emotional state to an emotional state that is classified as an improved emotional state.

[0538] In an embodiment, the Rider voice capture system 31136 comprises an intelligent agent 31140 that interacts with the Rider to obtain Rider feedback for use by a voice analysis circuit 31132 for Rider emotional state classification. In an embodiment, the voice analysis circuit 31132 uses a first machine learning system, and the expert system 31139 uses a second machine learning system. In an embodiment, the expert system 31139 is trained to optimize at least one behavior parameter 31124 based on feedback of the resulting emotional state when adjusting at least one behavior parameter 31124 for a set of individuals. In an embodiment, the Rider's emotional state 3166 is determined by a combination of the Rider's captured voice output 31128 and at least one other parameter. In an embodiment, at least one other parameter is the Rider's camera-based emotional state determination. In an embodiment, at least one other parameter is traffic information. In an embodiment, at least one other parameter is weather information. In an embodiment, at least one other parameter is the vehicle state. In an embodiment, at least one other parameter is at least one pattern of lidar physiological data. In an embodiment, at least one other parameter is the vehicle's path. In an embodiment, at least one other parameter is in-vehicle audio content. In an embodiment, at least one other parameter is the vehicle's speed. In an embodiment, at least one other parameter is the vehicle's acceleration. In an embodiment, at least one other parameter is the vehicle's deceleration. In an embodiment, at least one other parameter is the proximity to objects along the path. In an embodiment, at least one other parameter is the proximity to other vehicles along the route.

[0539] Embodiments provided herein include an artificial intelligence system 3136 for speech processing to improve rider satisfaction, comprising: a first neural network 3122, trained to classify emotional states based on analysis of human voices, detecting the rider's emotional state through recognition of aspects of the rider's speech output 31128 captured while the rider is in the vehicle 3110 that correlate with at least one of the rider's emotional states 3166; and a second neural network 3120 optimizing the vehicle's driving parameters 31124 in accordance with the emotional state 31126 detected by the rider 3144 to obtain a preferred emotional state for the rider. In embodiments, at least one of the neural networks is a convolutional neural network. In embodiments, the first neural network 3122 is trained through the use of a training dataset that associates emotional state classes with human speech patterns. In embodiments, the first neural network 3122 is trained through the use of a training dataset of speech recordings tagged with emotional state identification data. In an embodiment, the Rider's emotional state is determined by a combination of the Rider's captured audio output and at least one other parameter. In an embodiment, the at least one other parameter is the Rider's camera-based emotional state determination. In an embodiment, the at least one other parameter is traffic information. In an embodiment, the at least one other parameter is weather information. In an embodiment, the at least one other parameter is the vehicle state.

[0540] In an embodiment, at least one other parameter is at least one pattern of lidar physiological data. In an embodiment, at least one other parameter is the vehicle's path. In an embodiment, at least one other parameter is in-vehicle audio content. In an embodiment, at least one other parameter is the vehicle's speed. In an embodiment, at least one other parameter is the vehicle's acceleration. In an embodiment, at least one other parameter is the vehicle's deceleration. In an embodiment, at least one other parameter is the proximity to objects along the path. In an embodiment, at least one other parameter is the proximity to other vehicles along the route.

[0541] Referring here to Figure 32, an embodiment provided herein is a transport system 3211 having an artificial intelligence system 3236 for processing data from the interaction between the LiDAR and the vehicle with an e-commerce system to determine the state of the LiDAR and for optimizing at least one operating parameter of the vehicle to improve the state of the LiDAR. Another common activity for users of the device interface is e-commerce, such as shopping, bidding in auctions, and selling items. The e-commerce system uses search functions, undertakes advertising, and engages the user in various workflows that may ultimately lead to orders, purchases, bids, etc. As described herein regarding search, a set of in-vehicle-related search results, as well as in-vehicle-related advertising, may be provided for e-commerce. Furthermore, the in-vehicle-related interface and workflow may be configured based on the detection of the in-vehicle LiDAR, which may be entirely different from the workflow provided to the e-commerce interface configured for smartphones or desktop systems. Among other factors, the in-vehicle system may have access to information unavailable to conventional e-commerce systems, including route information (including direction, planned stops, and planned times), rider mood and behavior information (such as detected from past routes and from an in-vehicle sensor set), vehicle configuration and status information (such as manufacturer and model), and any of the other vehicle-related parameters described through this disclosure. For example, a rider who is bored (detected by an in-vehicle sensor set, such as using an expert system trained to detect boredom) and on a long journey (indicated by a route being taken by the vehicle) may be far more patient and likely to engage with deeper, richer content and longer workflows than a typical mobile user. Another example is that in-vehicle users may be far more likely to engage in free trials, surveys, or other actions that promote engagement with the brand. Also, in-vehicle users may have other time to accomplish specific purposes, such as buying something they need.Presenting the same interface, content, and workflow to in-vehicle users may result in missing out on excellent opportunities for deeper engagement, which are less common in other environments where many elements compete to capture the user's attention. In embodiments, an e-commerce system interface may be provided to in-vehicle users, and at least one of the interface display, content, search results, advertisements, and one or more associated workflows (such as for shopping, bidding, searching, purchasing, providing feedback, product display, rating, or review input) may be configured based on detection of the use of the in-vehicle interface. The display and interaction may be further configured based on detection of the display type (e.g., enabling richer or larger images for a large HD display), network capabilities (e.g., enabling faster loading and lower latency by caching lower-resolution images to be rendered initially), audio system capabilities (e.g., using audio for dialogue management and intelligent assistant interaction), and similar things related to the vehicle (optionally based on a set of rules or based on machine learning). Display elements, content, and workflows may be configured by machine learning, such as the use of A / B testing and / or genetic programming techniques, including configuring alternative dialogue types and tracking outcomes. The outcomes used to train the automated configuration of the in-vehicle e-commerce interface workflow may include engagement level, yield, purchase, rider satisfaction, ratings, and others. In-vehicle users may be profiled and clustered by behavioral profiling, demographic profiling, psychometric profiling, location-based profiling, collaborative filtering, similarity-based clustering, etc., as in conventional e-commerce, but the profiles may be extended by route information, vehicle information, vehicle configuration information, vehicle status information, rider information, etc.Sets of in-vehicle user profiles, groups, and clusters may be maintained separately from conventional user profiles to increase the likelihood that differences in in-vehicle shopping areas will be considered when targeting search results, advertisements, product offers, and discounts, thereby achieving learning about the content to be presented and how it should be presented.

[0542] Embodiments provided herein include a transport system 3211 comprising an artificial intelligence system 3236 for processing data from the interaction between a lidar 3244 and a vehicle's e-commerce system to determine the lidar state, and for optimizing at least one operating parameter of the vehicle to improve the lidar state.

[0543] Embodiments provided herein include a rider satisfaction system 32123 for optimizing rider satisfaction 32121, the rider satisfaction system comprising: an e-commerce interface 32141 deployed for access by a rider in a vehicle 3210; a rider dialogue circuit for capturing rider interactions with the deployed interface 32141; a rider state determination circuit 32143 for processing captured rider interactions 32144 to determine a rider state 32145; and an artificial intelligence system 3236 trained to optimize at least one parameter 32124 that affects the driving of the vehicle in order to improve the rider state 3237 in response to the rider state 3237. In embodiments, the vehicle 3210 includes 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 an autonomous driving vehicle. In the embodiment, the e-commerce interface is self-adaptive and responds to at least one of the following: rider identity, vehicle route, rider mood, rider behavior, vehicle configuration, and vehicle status.

[0544] In an embodiment, the e-commerce interface 32141 provides in-vehicle relevant content 32146 based on at least one of the following: rider identity, vehicle route, rider mood, rider behavior, vehicle configuration, and vehicle status. In an embodiment, the e-commerce interface executes a user interaction workflow 32147 adapted for use by the rider 3244 of the vehicle 3210. In an embodiment, the e-commerce interface provides one or more results of a search query 32148 adapted for presentation in the vehicle. In an embodiment, the results of the search query adapted for presentation in the vehicle are presented to the e-commerce interface along with advertisements adapted for presentation in the vehicle. In an embodiment, the rider interaction circuit 32142 captures rider interactions 32144 with the interface in response to the content 32146 presented to the interface.

[0545] Figure 33 shows a method 3300 for optimizing vehicle parameters according to embodiments of the systems and methods disclosed herein. In 3302, the method includes capturing lidar interactions in an in-vehicle e-commerce system. In 3304, the method includes determining a lidar state based on the captured lidar interactions and at least one operating parameter of the vehicle. In 3306, the method includes processing the lidar state with a lidar satisfaction model adapted to suggest at least one operating parameter of the vehicle that affects the lidar state. In 3306, the method includes optimizing the proposed at least one operating parameter for at least one of maintaining and improving the lidar state.

[0546] Refer to Figures 32 and 33. 33. Embodiments provided herein include an artificial intelligence system 3236 for improving rider satisfaction, comprising: a first neural network 3222 trained to classify rider states based on an analysis of rider interactions 32144 with an in-vehicle e-commerce system to detect rider states 32149 through the recognition of aspects of rider interactions 32144 captured while the rider is in the vehicle that correlate with at least one rider state 3237; and a second neural network 3220 that optimizes vehicle operating parameters in response to detected rider states to achieve a preferred rider state.

[0547] Referring to Figure 34, embodiments provided herein include a transport system 3411 having an artificial intelligence system 3436 for processing data from at least one Internet of Things (IoT) device 34150 in the environment 34151 of the vehicle 3410 to determine the state of the vehicle 34152, and optimizing at least one operating parameter 34124 of the vehicle to improve the state of the LiDAR 3437 based on the determined state of the vehicle 34152.

[0548] Embodiments provided herein are transport systems 3411, characterized in that they include an artificial intelligence system 3436 that processes data from at least one IoT device 34150 in the environment 34151 of a vehicle 3410 to determine a determined state 34152 of the vehicle, and optimizes at least one operating parameter 34124 of the vehicle to improve the state 3437 of the lidar based on the determined state 34152 of the vehicle 3410.

[0549] Figure 35 shows a method 3500 for improving the state of a rider through optimization of vehicle operation, according to embodiments of the systems and methods disclosed herein. In 3502, the method includes capturing vehicle operation-related data with at least one IoT device. In 3504, the method includes analyzing the captured data with a first neural network that determines the state of the vehicle based at least partially on a portion of the captured vehicle operation-related data. In 3506, the method includes receiving data describing the state of a rider occupying a moving vehicle. In 3508, the method includes using a neural network to determine at least one vehicle operation parameter that affects the state of a rider riding in a driving vehicle. In 3508, the method includes using an artificial intelligence-based system to optimize at least one vehicle operation parameter such that the optimization result consists of an improvement in the state of the rider.

[0550] Referring to Figures 34 and 35, in an embodiment, the vehicle 3410 constitutes a system for automating at least one control parameter 34153 of the vehicle 3410. In an embodiment, the vehicle 3410 is at least a semi-autonomous vehicle. In an embodiment, the vehicle 3410 is automatically routed. In an embodiment, the vehicle 3410 is an autonomous vehicle. In an embodiment, at least one IoT device 34150 is located in the vehicle's operating environment 34154. In an embodiment, at least one IoT device 34150 that captures data about the vehicle 3410 is located outside the vehicle 3410. In an embodiment, at least one IoT device is a dashboard camera. In an embodiment, at least one IoT device is a mirror camera. In an embodiment, at least one IoT device is a motion sensor. In an embodiment, at least one IoT device is a seat-based sensor system. In an embodiment, at least one IoT device is an IoT-enabled lighting system. In an embodiment, the lighting system is an interior lighting system. In an embodiment, the lighting system is a headlight lighting system. In an embodiment, at least one IoT device is a traffic signal camera or sensor. In an embodiment, at least one IoT device is a road camera. In an embodiment, the road surface camera is located on at least one of a telephone and a utility pole. In an embodiment, at least one IoT device is a road sensor. In an embodiment, at least one IoT device is an in-vehicle thermostat. In an embodiment, at least one IoT device is a toll booth. In an embodiment, at least one IoT device is a road sign. In an embodiment, at least one IoT device is a traffic control signal device. In an embodiment, at least one IoT device is a vehicle-mounted sensor. In an embodiment, at least one IoT device is a refueling system. In an embodiment, at least one IoT device is a recharging system. In an embodiment, at least one IoT device is a wireless charging station.

[0551] Embodiments provided herein include a rider state correction system 34155 for improving the state 3437 of a rider 3444 in a vehicle 3410, the system comprising: a first neural network 3422 that operates to classify the state of a vehicle through analysis of information about the vehicle taken in by an Internet of Things device 34150 while the vehicle 3410 is in operation; and a second neural network 3420 that operates to optimize at least one operating parameter 34124 of the vehicle based on the classified state of the vehicle 34152, information about the state of a rider riding in the vehicle, and information correlating the vehicle's operation with its effect on the rider state.

[0552] In an embodiment, the vehicle includes a system for automating at least one control parameter 34153 of the vehicle 3410. In an embodiment, the vehicle 3410 is at least a semi-autonomous vehicle. In an embodiment, the vehicle 3410 is automatically routed. In an embodiment, the vehicle 3410 is an autonomous vehicle. In an embodiment, at least one Internet of Things device 34150 is located within the operating environment of the vehicle 3410. In an embodiment, at least one IoT device 34150 that captures data about the vehicle 3410 is located outside the vehicle 3410. In an embodiment, at least one IoT device is a dashboard camera. In an embodiment, at least one IoT device is a mirror camera. In an embodiment, at least one IoT device is a motion sensor. In an embodiment, at least one IoT device is a seat-based sensor system. In an embodiment, at least one IoT device is an IoT-enabled lighting system.

[0553] In an embodiment, the lighting system is an interior lighting system. In an embodiment, the lighting system is a headlight lighting system. In an embodiment, at least one IoT device is a traffic signal camera or sensor. In an embodiment, at least one IoT device is a road camera. In an embodiment, the road surface camera is located on at least one of a telephone and a utility pole. In an embodiment, at least one IoT device is a road sensor. In an embodiment, at least one IoT device is an on-board thermostat. In an embodiment, at least one IoT device is a toll booth. In an embodiment, at least one IoT device is a road sign. In an embodiment, at least one IoT device is a traffic control signal device. In an embodiment, at least one IoT device is a vehicle-mounted sensor. In an embodiment, at least one IoT device is a refueling system. In an embodiment, at least one Internet of Things device is a recharging system. In an embodiment, at least one IoT device is a wireless charging station.

[0554] Embodiments provided herein include an artificial intelligence system 3436 comprising: a first neural network 3422 trained to determine the operating state 34152 of a vehicle 3410 from data about the vehicle captured in the vehicle's operating environment 34154, the first neural network 3422 operating to identify the operating state 34152 of the vehicle by processing information about the vehicle 3410 captured by at least one Internet of Things device 34150 while the vehicle is operating; a data structure 34156 that facilitates the determination of operating parameters affecting the operating state of the vehicle; and a second neural network 3420 operating to optimize at least one of the determined operating parameters 34124 of the vehicle based on the identified operating state 34152 by processing information about the state of a rider 3444 riding in the vehicle 3410, and information correlating the vehicle's operation with the rider's state.

[0555] In an embodiment, improvements in the lidar state are reflected in update data describing the lidar state captured in response to vehicle operation based on an optimized vehicle operation parameter. In an embodiment, improvements in the lidar state are reflected in data captured by at least one IoT device 34150, which is positioned to capture information about the lidar 3444 while occupying the vehicle 3410 in response to optimization. In an embodiment, the vehicle 3410 constitutes a system for automating at least one control parameter 34153 of the vehicle. In an embodiment, the vehicle 3410 is at least a semi-autonomous vehicle. In an embodiment, the vehicle 3410 is automatically routed. In an embodiment, the vehicle 3410 is an autonomous vehicle. In an embodiment, at least one IoT device 34150 is located in the vehicle's operating environment 34154. In an embodiment, at least one IoT device 34150 that captures data about the vehicle is located outside the vehicle. In an embodiment, at least one IoT device 34150 is a dashboard camera. In one embodiment, at least one IoT device 34150 is a mirror camera. In another embodiment, at least one IoT device 34150 is a motion sensor. In another embodiment, at least one IoT device 34150 is a sheet-based sensor system. In another embodiment, at least one IoT device 34150 is an IoT-enabled lighting system.

[0556] In an embodiment, the lighting system is an interior lighting system. In an embodiment, the lighting system is a headlight lighting system. In an embodiment, at least one IoT device 34150 is a traffic signal camera or sensor. In an embodiment, at least one IoT device 34150 is a road camera. In an embodiment, the road surface camera is located on at least one of a telephone and a utility pole. In an embodiment, at least one IoT device 34150 is a road sensor. In an embodiment, at least one IoT device 34150 is an on-board thermostat. In an embodiment, at least one IoT device 34150 is a toll booth. In an embodiment, at least one IoT device 34150 is a road sign. In an embodiment, at least one IoT device 34150 is a traffic control signal device. In an embodiment, at least one IoT device 34150 is a vehicle-mounted sensor. In an embodiment, at least one IoT device 34150 is a refueling system. In an embodiment, at least one IoT device 34150 is a recharging system. In one embodiment, at least one IoT device 34150 is a wireless charging station.

[0557] Referring to Figure 36, an embodiment provided herein is a transport system 3611 having an artificial intelligence system 3636 for processing sensory input from a wearable device 36157 in a vehicle 3610 to determine an emotional state 36126 and for optimizing at least one operating parameter 36124 of the vehicle 3610 to improve the rider's emotional state 3637. A wearable device 36157, such as any described throughout this disclosure, may be used both as an input to a real-time control system (such as any type of model-based, rule-based, or artificial intelligence system described herein) to detect any of the emotional states described herein (favorable or unfavorable) and to indicate the purpose of improving an unfavorable state or maintaining a favorable state, and as a feedback mechanism for training the artificial intelligence system 3636 to configure a set of operating parameters 36124 to promote or maintain a favorable state.

[0558] Embodiments provided herein include a transport system 3611 comprising: an artificial intelligence system 3636 for processing sensory input from a wearable device 36157 in a vehicle 3610 to determine the emotional state 36126 of a lidar 3644 in the vehicle 3610; and a system for optimizing vehicle driving parameters 36124 to improve the emotional state 3637 of the lidar 3644. In embodiments, the vehicle is an autonomous vehicle. In embodiments, the artificial intelligence system 3636 detects the emotional state 36126 of a lidar riding in an autonomous vehicle by recognizing a pattern of emotional state indication data from a pair of wearable sensors 36157 worn by the lidar 3644. In embodiments, the pattern indicates at least one of the lidar's preferred emotional state and the lidar's unpredictable emotional state. In an embodiment, the artificial intelligence system 3636 optimizes the vehicle's operating parameters 36124 in response to the detected emotional state of the rider in order to achieve at least one of the following: maintaining the rider's detected favorable emotional state, and achieving the rider's favorable emotional state following the detection of an unfavorable emotional state. In an embodiment, the artificial intelligence system 3636 includes an expert system that detects the rider's emotional state by processing rider emotional state instruction data received from a set of wearable sensors 36157 worn by the rider. In an embodiment, the expert system processes rider emotional state index data using at least one of a training set of emotional state indices for a set of riders and a rider emotional state index generated by a trainer. In an embodiment, the artificial intelligence system includes a recurrent neural network 3622 that detects the rider's emotional state.

[0559] In an embodiment, the recurrent neural network consists of multiple connected nodes that form a directed cycle, and the recurrent neural network further facilitates the bidirectional flow of data between the connected nodes. In an embodiment, the artificial intelligence system 3636 comprises a radial basis function neural network that optimizes the operation parameters 36124. In an embodiment, optimizing the operation parameters 36124 is based on a correlation between the vehicle operation state 3645 and the rider emotion state 3637. In an embodiment, the correlation is determined using a training set of emotion state indices for a set of riders and at least one of the rider emotion state indices generated by a human trainer. In an embodiment, the vehicle operation parameters to be optimized are determined and adjusted to induce a preferred rider emotion state.

[0560] In an embodiment, the artificial intelligence system 3636 further learns to classify patterns of emotional state index data from a training dataset 36131 supplied from at least one of the data flows 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 system, and to associate patterns with emotional states and their changes. In an embodiment, the artificial intelligence system 3636 detects patterns of rider emotional state indication data that indicate the rider's emotional state is changing from a first emotional state to a second emotional state, and the optimization of vehicle driving parameters is in response to the indicated change in emotional state. In an embodiment, patterns of rider emotional state indication data indicate that the rider's emotional state is changing, the rider's emotional state is stable, the rate of change in the rider's emotional state, the direction of change in the rider's emotional state, and the polarity of change in the rider's emotional state, indicating that the rider's emotional state is changing to an unfavorable state and the rider's emotional state is changing to a favorable state.

[0561] In an embodiment, the operational parameter 36124 to be optimized affects at least one of the following: the vehicle's path, in-vehicle audio content, vehicle speed, vehicle acceleration, vehicle deceleration, proximity to objects along the path, and proximity to other vehicles along the path. In an embodiment, the artificial intelligence system 3636 interacts with the vehicle control system to optimize the operational parameter. In an embodiment, the artificial intelligence system 3636 further includes a neural network 3622, which includes one or more perceptrons that mimic human sensations, facilitating the determination of the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated. In an embodiment, the set of wearable sensors 36157 includes at least two of the following: a wristwatch, ring, wristband, armband, ankle band, torso band, skin patch, head-mounted device, eyeglasses, footwear, gloves, in-ear device, clothing, headphones, belt, ring, thumb ring, and necklace. In an embodiment, the artificial intelligence system 3636 uses deep learning to determine patterns of wearable sensor-generated emotional state indication data that indicate the lidar's emotional state as at least one of a favorable emotional state and an unfavorable emotional state. In an embodiment, the artificial intelligence system 3636 responds to the emotional state indicated by the lidar by optimizing operational parameters to at least one of achieving and maintaining the emotional state indicated by the lidar.

[0562] In an embodiment, the artificial intelligence system 3636 adapts the characterization of the rider's preferred emotional state based on context collected from multiple sources, including data indicating the rider's purpose, time of day, traffic conditions, and weather, and optimizes the operating parameters 36124 for at least one of achieving and maintaining the adapted preferred emotional state. In an embodiment, the artificial intelligence system 3636 optimizes the operating parameters in real time in response to the detection of the rider's emotional state. In an embodiment, the vehicle is an autonomous vehicle. In an embodiment, the artificial intelligence system comprises: a first neural network 3622 that detects the rider's emotional state through expert system-based processing of rider emotional state indicative wearable sensor data from multiple wearable physiological state sensors attached to the vehicle, wherein the emotional state indicative wearable sensor data indicates at least one of the rider's preferred emotional state and the rider's unpredictable emotional state; and a second neural network 3620 that optimizes the vehicle's operating parameters 36124 in response to the detected emotional state of the rider, for at least one of achieving and maintaining the rider's preferred emotional state. In this embodiment, the first neural network 3622 is a recurrent neural network, and the second neural network 3620 is a radial basis function neural network.

[0563] In an embodiment, the second neural network 3620 optimizes the operation parameters 36124 based on the correlation between the vehicle operating state 3645 and the rider emotional state 3637. In an embodiment, the vehicle operation parameters to be optimized are determined and adjusted to induce a preferred rider emotional state. In an embodiment, the first neural network 3622 further learns to classify patterns in rider emotional state indicative wearable sensor data from a training dataset supplied from at least one of the data flows 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 system, and to associate the patterns with emotional states and their changes. In an embodiment, the second neural network 3620 optimizes the operation parameters in real time in response to the detection of the rider's emotional state by the first neural network 3622. In an embodiment, the first neural network 3622 detects patterns in rider emotional state wearable sensor data indicating that the rider's emotional state is changing from a first emotional state to a second emotional state. In this embodiment, the second neural network 3620 optimizes the vehicle's operating parameters in response to the indicated changes in emotional state.

[0564] In an embodiment, the first neural network 3622 comprises a plurality of connected nodes forming a directed cycle, and the first neural network 3622 further facilitates the bidirectional flow of data between connected nodes. In an embodiment, the first neural network 3622 includes one or more perceptrons that mimic human sensations to facilitate determining the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated. In an embodiment, wearable sensor data indicating the rider's emotional state indicates that the rider's emotional state is changing, the rider's emotional state is stable, the rate of change in the rider's emotional state, the direction of change in the rider's emotional state, and the polarity of change in the rider's emotional state, indicating that the rider's emotional state is changing to an undesirable state and the rider's emotional state is changing to a favorable state. In an embodiment, the driving parameters to be optimized affect at least one of the following: vehicle path, in-vehicle audio content, vehicle speed, vehicle acceleration, vehicle deceleration, proximity to objects along the path, and proximity to other vehicles along the path. In one embodiment, the second neural network 3620 interacts with the vehicle control system to adjust the operating parameters. In another embodiment, the first neural network 3622 includes one or more perceptrons that mimic human sensations to facilitate determining the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated.

[0565] In the embodiment, the vehicle is an autonomous vehicle. In the embodiment, the artificial intelligence system 3636 detects, at least partially, a change in the emotional state of a rider riding in the autonomous vehicle by recognizing a pattern of emotional state indication data from a set of wearable sensors worn by the rider. In the embodiment, the pattern indicates at least one of a decline in the rider's preferred emotional state and the onset of the rider's undesirable emotional state. In the embodiment, the artificial intelligence system 3636 determines at least one operating parameter 36124 of the autonomous vehicle that indicates the change in emotional state, based on the correlation between the pattern of emotional state data and a set of vehicle operating parameters. In the embodiment, the artificial intelligence system 3636 determines to adjust at least one operating parameter 36124 to achieve at least one of restoring the rider's preferred emotional state and reducing the onset of the rider's undesirable emotional state.

[0566] In an embodiment, the correlation of patterns in rider emotional state index wearable sensor data is determined using a training set of emotional state wearable sensor indices for a set of riders and at least one of rider emotional state wearable sensor indices generated by a human trainer. In an embodiment, the artificial intelligence system 3636 further learns to classify patterns in emotional state index wearable sensor data from a training dataset supplied from at least one of data flows 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 system, and to associate the patterns with changes in rider emotional state. In an embodiment, patterns in wearable sensor data indicating a rider's emotional state indicate that the rider's emotional state is changing, the rider's emotional state is stable, the rate of change in the rider's emotional state, the direction of change in the rider's emotional state, and the polarity of change in the rider's emotional state, the rider's emotional state is changing to an unfavorable state, and the rider's emotional state is changing to a favorable state.

[0567] In embodiments, operational parameters determined from processing wearable sensor data indicating the rider's emotional state affect at least one of the following: the vehicle's route, in-vehicle audio content, vehicle speed, vehicle acceleration, vehicle deceleration, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, the artificial intelligence system 3636 further interacts with the vehicle control system to adjust the operational parameters. In embodiments, the artificial intelligence system 3636 further includes a neural network including one or more perceptrons that mimic human sensations, which facilitates determining the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated.

[0568] In an embodiment, the set of wearable sensors includes at least two of the following: a watch, a ring, a wristband, an armband, ankle band, a torso band, a skin patch, a head-mounted device, eyeglasses, footwear, gloves, an ear-hook device, clothing, headphones, a belt, a finger ring, a thumb ring, and a necklace. In an embodiment, the artificial intelligence system 3636 uses deep learning to determine patterns in wearable sensor-generated emotional state indication data that indicate changes in the rider's emotional state. In an embodiment, the artificial intelligence system 3636 further determines changes in the rider's emotional state based on context collected from multiple sources, including data indicating the purpose of the rider riding in the autonomous vehicle, the time of day, traffic conditions, and weather, and optimizes the operation parameters 36124 to achieve and maintain a fitted, preferred emotional state. In an embodiment, the artificial intelligence system 3636 adjusts the operation parameters in real time in response to the detection of changes in the rider's emotional state.

[0569] In an embodiment, the vehicle is an autonomous vehicle. In an embodiment, the artificial intelligence system 3636 includes a recurrent neural network for indicating changes in the emotional state of a lidar in an autonomous vehicle by recognizing patterns of wearable sensor data indicating emotional states from a set of wearable sensors worn by the lidar. In an embodiment, the patterns indicate at least one of a first degree of the lidar's preferred emotional state and a second degree of the lidar's unfavorable emotional state, and the system includes a radial basis function neural network that optimizes the vehicle's operating parameters 36124 to achieve a target emotional state for the lidar in response to instructions for changes in the lidar's emotional state.

[0570] In an embodiment, a radial basis function neural network optimizes operating parameters based on a correlation between the vehicle's operating state and the rider's emotional state. In an embodiment, the target emotional state is a preferred rider emotional state, and the vehicle driving parameters to be optimized are determined and adjusted to induce the preferred rider emotional state. In an embodiment, a recurrent neural network further learns to classify patterns in wearable sensor data indicating emotional states and associate them with changes in emotional states, from a training dataset supplied from at least one of the following data streams: unstructured data sources, social media sources, wearable devices, in-vehicle sensors, rider helmets, rider headgear, and rider voice systems. In an embodiment, the radial basis function neural network optimizes operating parameters in real time in response to the recurrent neural network's detection of changes in the rider's emotional state. In an embodiment, the recurrent neural network detects patterns in emotional state index wearable sensor data indicating that the rider's emotional state is changing from a first emotional state to a second emotional state. In an embodiment, the radial basis function neural network optimizes the vehicle's operating parameters in response to the indicated change in emotional state. In one embodiment, the recurrent neural network comprises multiple connected nodes that form a directed cycle, and the recurrent neural network further facilitates the bidirectional flow of data between the connected nodes.

[0571] In embodiments, patterns of wearable sensor data indicating emotional state indicate that the rider's emotional state is changing, stable, the rate of change in the rider's emotional state, the direction of change in the rider's emotional state, and the polarity of change in the rider's emotional state, indicating that the rider's emotional state is changing to an undesirable state and that the rider's emotional state is changing to a favorable state. In embodiments, the operational parameters to be optimized affect at least one of the vehicle's path, in-vehicle audio content, vehicle speed, vehicle acceleration, vehicle deceleration, proximity to objects along the path, and proximity to other vehicles along the path. In embodiments, a radial basis function neural network interacts with the vehicle control system to tune the operational parameters. In embodiments, a recurrent neural network includes one or more perceptrons that mimic human sensations to facilitate determining the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated.

[0572] In one embodiment, the artificial intelligence system 3636 maintains the rider's preferred emotional state by using a modular neural network, the modular neural network comprising a rider emotional state determination neural network that processes wearable sensor data indicating the rider's emotional state in the vehicle and detects patterns. In another embodiment, the system comprises an intermediary circuit that converts output data from the rider emotional state determination neural network into vehicle operating state data, wherein patterns found in the emotional state index wearable senso...

Claims

1. A system for representing attributes in a digital twin of a transport system, comprising a digital twin data store and one or more processors, wherein the digital twin data store stores a transport system digital twin including real-world element digital twins, the transport system digital twin corresponds to a transport system, each real-world element digital twin provides a digital twin of each real-world element located within the transport system, the real-world element digital twin includes a mobile element digital twin, each mobile element digital twin provides a digital twin of each mobile element within the real-world element, and the one or more processors are configured to determine the location of each mobile element in response to the occurrence of a trigger condition, and to update the mobile element digital twin corresponding to the mobile element in response to determining the location of the mobile element to reflect the location of the mobile element.

2. A system for representing attributes in a digital twin of a transport system according to claim 1, wherein the moving element is a worker in the transport system.

3. A system for representing attributes in a digital twin of a transport system according to claim 1, wherein the moving element is a vehicle within the transport system.

4. A system for representing attributes in a transport system digital twin according to claim 1, wherein the trigger condition is the expiration of a dynamically determined time interval.

5. A system for representing attributes in a digital twin of a transport system according to claim 4, wherein the dynamically determined time interval increases in response to determining a single moving element within the transport system.

6. A system for representing attributes in a transport system digital twin according to claim 4, wherein the dynamically determined time interval increases in response to determining the occurrence of a predetermined period of reduced environmental activity.

7. A system for representing attributes in a transport system digital twin according to claim 4, wherein the dynamically determined time interval decreases in response to determining abnormal activity within the transport system.

8. A system for representing attributes in a digital twin of a transport system according to claim 4, wherein the dynamically determined time interval is a first time interval, and the dynamically determined time interval decreases to a second time interval in response to determining the movement of the moving element.

9. A system for representing attributes in a transport system digital twin according to claim 4, wherein the dynamically determined time interval increases from a second time interval to a first time interval in response to determining non-movement of the moving element for at least a third time interval.

10. A system for representing attributes in a digital twin of a transport system according to claim 1, wherein the trigger condition is the expiration of a time interval.

11. The aforementioned time interval is calculated based on the probability that the moving element has moved, a system for representing attributes in a digital twin of a transport system according to claim 10.

12. A system for representing attributes in a digital twin of a transport system according to claim 1, wherein the trigger condition is the proximity of the moving element to another moving element.

13. The trigger condition is a system for representing attributes in a digital twin of a transport system according to claim 1, based on the density of moving elements within the transport system.

14. Route information obtained from the navigation module of the aforementioned moving element is a system for representing attributes in the digital twin of the transport system described in claim 1.

15. A system for representing the attributes of a digital twin of the transport system according to claim 1, wherein the one or more processors are further configured to acquire route information, and acquiring the route information includes using a plurality of sensors in the transport system to detect the movement of the moving element, to acquire the destination of the moving element, to use a plurality of sensors in the transport system to calculate an optimized route for the moving element, and to instruct the moving element to navigate the optimized route.

16. A system for representing the attributes of a digital twin of a transport system according to claim 1, wherein the optimized route includes using route information of other movement elements within the real-world elements.

17. The optimized route is a system for representing the attributes of a digital twin of a transport system according to claim 1, which minimizes interaction between mobile elements and humans within the transport system.

18. A system for representing attributes in a digital twin of a transport system according to claim 1, wherein the transport elements include an autonomous vehicle and a non-autonomous vehicle, and the optimized route reduces interaction between the autonomous vehicle and the non-autonomous vehicle.

19. A system for representing the attributes of a transport system digital twin according to claim 1, wherein the transport modeling includes the use of a particle transport model, a trigger response transport element tracking transport model, a macroscopic transport model, a microscopic transport model, a mesoscopic transport model, or a combination thereof.

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