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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    Title :

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


    Contributors:


    Publication date :

    2023-09-24


    Size :

    4400172 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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