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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    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




    Autonomous Driving with Deep Reinforcement Learning

    Zhu, Yuhua / Technische Universität Dresden | SLUB | 2023


    Multiagent reinforcement learning for autonomous driving in traffic zones with unsignalized intersections

    Spatharis, Christos / Blekas, Konstantinos | Taylor & Francis Verlag | 2024



    Uncertainty-Aware Decision-Making for Autonomous Driving at Uncontrolled Intersections

    Tang, Xiaolin / Zhong, Guichuan / Li, Shen et al. | IEEE | 2023