Autonomous Vehicle Behavior Monitoring With Scenario-Based Agents
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently monitoring and reporting errors or anomalies during operation, relying on post-error interventions and manual overrides, which can lead to unpredictable and undesirable outcomes.
Innovation Solution
A monitoring system utilizing Measurable Scenario Description Language (MSDL) to generate agents that simulate various scenarios, allowing for systematic monitoring of autonomous vehicle behavior and detection of anomalies by comparing actual behavior to predefined expectations, enabling real-time reporting and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional error monitoring methods are used (relying on operator intervention and manual overrides), then the system can detect errors after they occur, but the monitoring efficiency is low and errors are not captured proactively
Solution Approach 1:
The patent implements preliminary action by establishing a monitoring system that proactively checks for errors before they manifest as actual incidents. The system continuously monitors autonomous vehicle operations against predefined safety criteria and operational parameters, detecting potential errors in real-time rather than waiting for operator intervention after an error occurs. This allows the system to identify and address issues during the testing phase before they reach deployed vehicles.
Solution Approach 2:
The patent implements feedback mechanisms by creating a closed-loop monitoring system that continuously compares actual autonomous vehicle behavior against expected behavior patterns. The system provides real-time feedback on vehicle performance, capturing error data that is then fed back into the testing and validation processes. This feedback loop enables continuous improvement of the autonomous vehicle system by systematically analyzing captured errors and updating safety protocols.
2Reliability
If extensive testing is conducted in controlled environments to systematically monitor vehicle performance, then the reliability of error detection improves, but the time and resources required for testing increase
Solution Approach 1:
The patent implements copying by creating virtual replicas of testing scenarios through simulation environments. Instead of physically testing every possible scenario with actual autonomous vehicles, the system uses computational models to copy and reproduce diverse driving conditions, edge cases, and hazardous situations. This allows extensive systematic monitoring to be conducted in silico, significantly reducing the time and physical resources required while maintaining high reliability through repeated virtual testing.
Solution Approach 2:
The patent applies preliminary action by conducting comprehensive systematic monitoring and validation in controlled testing environments before deploying autonomous vehicles to real-world operations. The monitoring system is fully established and validated during the testing phase, allowing all necessary error detection protocols to be configured and tested in advance. This preliminary setup ensures that once deployed, the system can operate reliably without requiring extensive ongoing adjustment or retesting.
Data Source
AI summary
A system and methods thereof for monitoring proper behavior of an autonomous vehicle are provided. The method includes generating a plurality of agents, wherein each of the plurality of agents describes a physical object, wherein at least one of the plurality of agents is an agent for the DUT, generating a plurality of scenarios, wherein each scenario models a behavior of at least one of the plurality of agents, and monitoring an interaction between the plurality of agents and the DUT agent for a scenario modeling the respective agent.