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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    Titel :

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


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wong, Pak Kin (Herausgeber:in) / Xu, Jun (Herausgeber:in) / Zeng, Zhaotai (Autor:in) / Shi, Qing (Autor:in) / Zhuang, Weichao (Autor:in) / Wang, Xiaopeng (Autor:in) / Fan, Xuan (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    01.07.2025


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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