A left-turn decision-making model that considers information about surrounding vehicles at complex urban intersections is critical for the development of autonomous vehicles. Here, we propose an approach of developing algorithms for left-turn tactical-level decision-making to ensure autonomous vehicles can cross intersections safely. We collected trajectories data were at intersections. Then, a GPR model was trained to predict the trajectories of oncoming straight vehicles. After that, we proposed a conflict resolution method for left-turn behavior of autonomous vehicles at urban intersections and a driving action selection method that considers multiple factors, such as safety, efficiency, comfort and altruism. Finally, we built a simulation and verification platform for algorithm. The reliability and effectiveness of the tactical-level decision-making model was verified by both simulations and real road experiments. The results from this study can provide a theoretical basis for the in-depth study of left-turn driving decision-making in complex and uncertain intersection environments.


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    Titel :

    A Decision-Making Model of Left-Turn Behavior for Autonomous Vehicles at Urban Intersections Using a Single-Vehicle Scenario


    Beteiligte:
    Chen, Xue-Mei (Autor:in) / Ou, Yang-Jia-Xin (Autor:in) / Wang, Zi-Jia (Autor:in) / Li, Meng-Xi (Autor:in)

    Kongress:

    21st COTA International Conference of Transportation Professionals ; 2021 ; Xi’an, China


    Erschienen in:

    CICTP 2021 ; 2461-2471


    Erscheinungsdatum :

    14.12.2021




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch