Before deploying autonomous vehicles (AV), comprehensive safety assessments are essential. To enhance efficiency, generating safety-critical scenarios to accelerate testing is crucial. This paper proposes an adversarial strategy based on deep reinforcement learning to generate safety-critical scenarios for evaluating autonomous vehicle performance. First, a highway ramp merging scenario is modeled using a Markov process. Second, deep reinforcement learning is used to train the background vehicle (BV) to generate adversarial behaviors against the autonomous vehicle. A reasonable reward mechanism is introduced to prevent extreme dangerous behaviors and ensure scenario rationality. Simulation results show that the generated safety-critical scenarios significantly increase collision rates and reduce the performance of AV. Additionally, this method can generate scenarios with varying levels of danger, supporting more refined testing.


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    Title :

    Adversarial Generation for Autonomous Vehicles in Safety-Critical Ramp Merging Scenarios


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wong, Pak Kin (editor) / Xu, Jun (editor) / Zeng, Zhaotai (author) / Shi, Qing (author) / Zhuang, Weichao (author) / Wang, Xiaopeng (author) / Fan, Xuan (author)

    Conference:

    International Conference on Electric Vehicle and Vehicle Engineering ; 2024 ; Zhuhai, China December 20, 2024 - December 22, 2024



    Publication date :

    2025-07-01


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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