This paper focuses on safety performance testing and characterization of black-box highly automated vehicles (HAV). Existing testing approaches typically deploy the HAV into a specific testing environment involving various passively defined testing strategies presented by other traffic participants such as (i) a naturalistic driving policy learned from human drivers, (ii) extracted concrete scenarios from real-world driving data, and (iii) model-based or data-driven adversarial testing methodologies focusing on forcing safety-critical events. The safety performance of the HAV is further characterized by analyzing the obtained testing outcomes with a particular selected measure, such as the observed collision risk. These testing practices suffer from the scarcity of safety-critical events, have limited operational design domain (ODD) coverage, or are biased toward long-tail unsafe cases. This paper presents a novel and informative testing strategy that differs from these existing practices. The proposal is inspired by the intuition that a relatively safer HAV driving policy would allow the traffic vehicles to exhibit a higher level of aggressiveness to achieve a certain fixed level of an overall safety outcome metric. One can specifically characterize such an HAV and traffic interaction strategy and use it as a safety performance indicator for the HAV. Under the proposed testing scheme, the HAV is evaluated under its full ODD with a reward function that represents a trade-off between safety and adversity in generating safety-critical events. The proposed methodology is demonstrated in simulation with various HAV designs under different operational design domains.


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    Title :

    Training Adversarial Yet Safe Agent to Characterize the Safety Performance of Highly Automated Vehicles


    Contributors:


    Publication date :

    2024-10-22


    Size :

    1861400 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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