This paper presents a human-machine cooperative trajectory planning system for advanced driving assistance systems in lane-changing scenarios. Firstly, it establishes a vehicle dynamics model and provides evaluation indicators for safe and comfortable driving under lane-changing conditions. Secondly, it constructs a human-machine collaborative driving framework, designs a trajectory pre-generation strategy, and develops a learning-based weight generation model for human-machine collaborative driving. Finally, trajectory planning is implemented based on fuzzy logic theory and PID control. The system is validated through a Simulink/Carsim co-simulation experimental platform with driver-in-the-loop manner. The results demonstrate that the human-machine cooperative driving approach offers better performance in driving stability and safety compared to both autonomous driving and human driving alone.


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    Titel :

    Human-Machine Cooperative Trajectory Planning for Advanced Driving Assistance System in Lane Changing Scenarios


    Beteiligte:
    Li, Zhenghan (Autor:in) / Zhang, Ying (Autor:in) / Hua, Yuanhang (Autor:in) / Qi, Haoran (Autor:in) / Fu, Chenxiu (Autor:in) / Feng, Yun (Autor:in)


    Erscheinungsdatum :

    25.10.2024


    Format / Umfang :

    1332102 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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