In this paper, we propose a novel behavior planner that combines game theory with search-based planning for automated lane merging. Specifically, inspired by human drivers, we model the interaction between vehicles as a gap selection process. To overcome the challenge of multi-modal behavior exhibited by the surrounding vehicles, we formulate the trajectory selection as a matrix game and compute an equilibrium. Next, we validate our proposed planner in the high-fidelity simulator CARLA and demonstrate its effectiveness in handling interactions in dense traffic scenarios.


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    Titel :

    An Efficient Game-Theoretic Planner for Automated Lane Merging with Multi-Modal Behavior Understanding


    Beteiligte:
    Zhang, Luyao (Autor:in) / Han, Shaohang (Autor:in) / Grammatico, Sergio (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    4400172 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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