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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    Title :

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


    Contributors:


    Publication date :

    2021-07-11


    Size :

    2596009 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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