With the advent of machine learning, several autonomous driving tasks have become easier to accomplish. Nonetheless, the proliferation of autonomous vehicles in urban traffic scenarios has precipitated other challenges, such as cooperative driving. Multi-Agent Reinforcement Learning (MARL) approaches have emerged as a promising solution, as they can successfully address the challenge by simulating the interactive relationship among autonomous vehicles (AVs). In this paper, we leverage Social Value Orientation to depict the behavioral tendencies of AVs, thereby enhancing the performance of MARL approaches. We also incorporate different scale features in the policy network to strengthen its representation ability. Moreover, effective reward functions are designed based on traffic efficiency, comfort, safety, and strategy. Finally, we validate our approach in an open-source autonomous driving simulator. Simulation results indicate that our proposed approach outperforms IPPO and MAPPO algorithms in terms of success rate, route completion rate, crash rate, and other metrics.


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    Titel :

    Mastering Cooperative Driving Strategy in Complex Scenarios using Multi-Agent Reinforcement Learning


    Beteiligte:
    Liang, Qingyi (Autor:in) / Jiang, Zhengmin (Autor:in) / Yin, Jianwen (Autor:in) / Xu, Kun (Autor:in) / Pan, Zhongming (Autor:in) / Dang, Shaobo (Autor:in) / Liu, Jia (Autor:in)


    Erscheinungsdatum :

    17.07.2023


    Format / Umfang :

    1559419 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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