Autonomous Vehicle Controller Exception Handling via Belief State
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently and accurately navigating through vehicle transportation networks due to the lack of robust exception handling in existing operational management systems, which can lead to inefficiencies and inaccuracies when encountering unexpected operational conditions.
Innovation Solution
An introspective autonomous vehicle operational management system that includes an controller with a defined set of exceptions and handlers, where the controller determines the active handler based on the current belief state, switching between primary and exception handlers to manage exceptional conditions, and updates the belief state based on indicators from the handlers to navigate the network effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing operational management systems are used without robust exception handling, then the system structure remains simple, but the reliability and accuracy of autonomous navigation deteriorate when encountering unexpected conditions
Solution Approach 1:
The operational management system is segmented into distinct components: a belief state module that maintains probabilistic representations of operational conditions, an exception detection module that identifies exceptional conditions, and multiple handler modules (primary handlers and exception handlers) that process different types of conditions. This segmentation allows the system to handle exceptions reliably while maintaining a structured, manageable architecture.
Solution Approach 2:
The system dynamically switches between different handlers based on the current belief state. When an exceptional condition is detected, the system transitions from using a primary handler to using an appropriate exception handler. This dynamic adaptation allows the system to respond appropriately to changing operational conditions without requiring a completely complex static structure.
2Measurement precision
If the system uses a defined set of exceptions and handlers to manage exceptional conditions, then the accuracy of navigating unexpected conditions improves, but the complexity of the control system increases
Solution Approach 1:
The system performs preliminary actions by maintaining a predefined belief state that represents operational conditions before exceptions occur. Exception handlers are pre-configured to handle specific exceptional conditions, allowing the system to quickly and accurately detect and respond to exceptions without requiring complex real-time analysis. The belief state is updated in advance based on operational data, enabling proactive exception management.
3Adaptability or versatility
If the controller continuously updates the belief state based on handler indicators, then the adaptability to changing operational conditions improves, but the computational load and processing time increase
Solution Approach 1:
The system implements feedback by having handlers provide indicators about operational conditions, which are then used to update the belief state. This feedback loop allows the system to adapt to changing conditions efficiently, as updates occur only when handlers detect relevant changes rather than continuously. The belief state is refined based on actual operational feedback, enabling adaptability while minimizing unnecessary computational energy consumption.
Data Source
AI summary
Introspective autonomous vehicle operational management includes operating an introspective autonomous vehicle operational management controller including a policy for a model of an introspective autonomous vehicle operational management domain. Operating the controller includes, in response to a determination that a current belief state of the policy indicates an exceptional condition, identifying an exception handler for controlling the autonomous vehicle. Operating the controller includes, in response to a determination that the current belief state indicates an unexceptional condition, identifying a primary handler as the active handler. Operating the controller includes controlling the autonomous vehicle to traverse a current portion of the vehicle transportation network in accordance with the active handler, receiving an indicator output by the active handler, generating an updated belief state based on the indicator, and controlling the autonomous vehicle to traverse a subsequent portion of the vehicle transportation network based on the updated belief state.