Highlights Cooperative decision-making of multiple autonomous vehicles can be realized using a coalition game-based model. The driving risk from the perspective of different manual vehicles is quantified, thereby guiding the decision-making of autonomous vehicles in the lane-changing process. The impact of manual vehicles’ randomness on the cooperative decision-making of multiple autonomous vehicles was analyzed. A conflict decoupling algorithm for conflicts in a connected mixed traffic environment is proposed.

    Abstract Advances in vehicle-networking technologies have enabled vehicles to cooperate in mixed traffic. However, realizing the cooperative decision-making of multiple connected autonomous vehicles (CAVs) when influenced by the presence of connected manual vehicles (CMVs) is a challenging area in current research. In this study, we propose a coalition game-based (CG-based) model for multi-CAV cooperative decision-making in a connected mixed traffic environment. First, the model integrates the perceived risk field theory, quantifying the driving risk from the perspective of different CMVs; this risk is used to determine the uncertainty of the motion state of CMVs. Second, the model can identify the conflicts caused by multiple lane-changing vehicles and decouple the conflict problem into multiple two-vehicle lane-changing games, including a cooperative game between two CAVs and a non-cooperative game between a CAV and a CMV. To test the proposed model, four scenarios that blocked the passage of multiple CAVs were set up; in these scenarios, the average speed of the CG-based model was 21.05, 16.76, 23.17, and 12.55% higher than that of the LC2013 model. The simulation results showed that the CG-based model could improve the efficiency of multiple CAVs while ensuring safety in a mixed traffic flow.


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    Titel :

    Cooperative decision-making of multiple autonomous vehicles in a connected mixed traffic environment: A coalition game-based model


    Beteiligte:
    Fu, Minghao (Autor:in) / Li, Shiwu (Autor:in) / Guo, Mengzhu (Autor:in) / Yang, Zhifa (Autor:in) / Sun, Yaxing (Autor:in) / Qiu, Chunxiang (Autor:in) / Wang, Xin (Autor:in) / Li, Xin (Autor:in)


    Erscheinungsdatum :

    2023-11-10




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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