Driving through complex urban environments is a challenging task for autonomous vehicles (AVs), as they must safely reach their mission goal, and react properly to traffic participants while obeying traffic rules. Deep reinforcement learning (DRL) is a promising method to generate driving policies for AVs because it can explore complex environments and learn suitable reactions. In this work, we present a DRL algorithm for AVs to handle intersection scenarios while considering traffic rules. Furthermore, we enhance the safety of our DRL algorithm's decisions by introducing a safety checker based on a responsibility-sensitive safety (RSS) model. Evaluations show that our DRL algorithm outperforms the baseline method by driving safely to reach the mission goal while obeying the traffic rules at an intersection.


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    Titel :

    Safe and Rule-Aware Deep Reinforcement Learning for Autonomous Driving at Intersections


    Beteiligte:
    Zhang, Chi (Autor:in) / Kacem, Kais (Autor:in) / Hinz, Gereon (Autor:in) / Knoll, Alois (Autor:in)


    Erscheinungsdatum :

    2022-10-08


    Format / Umfang :

    1206218 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch