Cloud Diagnostic System for Autonomous Vehicle TaaS Link Validation
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Solution Overview
Problem
The integration of cloud technology and autonomous vehicle systems poses challenges in diagnosing issues due to the complexity of hybrid cloud environments and the lack of centralized visibility into data transmission between multiple cloud services, making it difficult to determine the root cause of problems, especially when issues arise from private or closed systems.
Innovation Solution
A system and method for testing and debugging autonomous vehicle systems by transmitting initiating messages from a global manager cloud to an external service cloud via an on-vehicle modem, using simulated messages to determine confidence thresholds for TaaS links, validating service and compute data, and updating TaaS components based on identified problems, thereby enabling efficient monitoring and debugging of multiple public clouds and integrating legacy on-vehicle systems with cloud connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud infrastructure and multiple cloud services are integrated into autonomous vehicle systems, then the functionality and service capabilities are improved, but the system complexity and difficulty of diagnosing issues increase
Solution Approach 1:
The patent introduces an intermediary diagnostic system that acts as a mediator between multiple cloud services and the autonomous vehicle. This intermediary captures, correlates, and analyzes data from various cloud providers (AWS, Azure, GCP) and on-vehicle systems, transforming the complex multi-cloud environment into a manageable diagnostic framework without reducing the underlying service capabilities.
Solution Approach 2:
The diagnostic system segments the complex autonomous vehicle system into distinct functional modules (perception, planning, control, cloud services) and traces issues through each segment independently. This segmentation allows developers to isolate problems to specific components or cloud services while maintaining the integrated functionality of the complete system.
2Adaptability or versatility
If data transmission between multiple cloud services is distributed across different platforms, then the service flexibility is improved, but the visibility and traceability of data flow deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the diagnostic system continuously monitors data flow between cloud services and vehicle systems, capturing metadata about data transmission. This feedback loop provides real-time visibility into data flow paths, allowing developers to trace information across multiple cloud platforms while maintaining the flexibility of distributed architecture.
Solution Approach 2:
The diagnostic system creates copies of data flow metadata and transmission records without interfering with the actual data transmission between cloud services. These copies enable comprehensive tracking and analysis of data flow paths across AWS, Azure, and GCP while the original flexible distributed communication remains intact.
3Measurement precision
If centralized diagnosis is implemented across multiple cloud services, then the ability to identify root causes is improved, but the computational resources and processing time required increase
Solution Approach 1:
The diagnostic system performs preliminary actions by pre-configuring diagnostic rules, thresholds, and analysis algorithms before issues occur. When problems arise, the system applies these pre-prepared diagnostic frameworks to rapidly analyze data flows and identify root causes without requiring intensive real-time computational resources for creating diagnostic logic.
Solution Approach 2:
The patent applies local quality by implementing specialized diagnostic algorithms tailored to specific cloud services and vehicle subsystems. Each diagnostic module is optimized for its specific domain (e.g., perception system diagnostics, cloud communication diagnostics), allowing efficient localized analysis rather than applying a single resource-intensive universal diagnostic approach to the entire system.
Data Source
AI summary
Provided are systems, methods, and computer program products for monitoring, testing, or debugging transportation services, generating or transmitting an initiating message from a global manager cloud to an external service cloud, to invoke a transportation as a service (TaaS) message from external service clouds that comprise confirmation, also including generating or transmitting a simulated message from the global manager cloud to mirror the TaaS message, or a portion, transmitted on a TaaS link from the external service cloud to the on-vehicle modem, determining, a confidence threshold for a capability or security of the TaaS link, validating AV service data sent from the global manager cloud to a TaaS component in an on-vehicle black box of the autonomous vehicle system, validating AV compute data sent from the autonomous vehicle system to the TaaS component in the on-vehicle black box, validating TaaS message data received from the external service cloud.