Connected and autonomous vehiles (CAVs) require an effective cooperative action planning strategy in an emergency situation. Monte Carlo Tree Search (MCTS) is a promising planning technique for such problems with large state spaces. However, traditional MCTS-based techniques do not scale well with the number of vehicles. In this paper, we present a novel MCTS-based cooperative action planning algorithm for CAV s driving in a coalition formation. Our proposed algorithm improves the reliability and the scalability of M CTS. Explicit communication is used to ensure that mitigation action plans chosen by the CAVs are conflict-free when possible. We perform the evaluation of the proposed algorithm in a large scale multi-agent based traffic simulation system. Our simulated experiments show that our approach improves upon current state-of-the-art centralized and decentralized algorithms.


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    Titel :

    Scalable Monte Carlo Tree Search for CAV s Action Planning in Colliding Scenarios


    Beteiligte:


    Erscheinungsdatum :

    11.07.2021


    Format / Umfang :

    2596009 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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