Abstract This paper presents a hierarchical and game-theoretic decision-making strategy for connected and automated vehicles (CAVs). A CAV can receive preview information using vehicle-to-everything (V2X) communication systems, and the optimal short- and long-term trajectory can be planned using this information. Specifically, in this study, the aggressiveness of all preceding vehicles in the car-following scenario can be estimated globally by monitoring the history of their time-series behaviors, before the CAV initiates a particular action, which is performed at the upper layer of the proposed decision-making structure. If it is determined that initiating a specific action is advantageous, the action is initiated, and the CAV then interacts with the vehicles locally to achieve its driving goal in a game-theoretical manner at the lower layer. In multiple test scenarios, we demonstrate the usefulness of our approach compared to the conventional decision-making approaches, and it shows a significant improvement in terms of success rates.

    Highlights Game-theoretic overtaking decision-making strategy for the connected and automated vehicle. The hierarchical estimation of the aggressiveness of potential interactive vehicles. Adaptive decision-making strategy whether initiating overtaking under uncertainty.


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    Title :

    Hierarchical and game-theoretic decision-making for connected and automated vehicles in overtaking scenarios


    Contributors:
    Ji, Kyoungtae (author) / Li, Nan (author) / Orsag, Matko (author) / Han, Kyoungseok (author)


    Publication date :

    2023-03-15




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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