In the cooperative closed-loop system of intelligent vehicle, driver behavior will directly affect the vehicle's motion state, and fatigue driving constitutes a significant contributing factor to the incidence of road traffic accidents. Therefore, this paper proposes a human-machine shared cooperative lateral torque control method that takes into account driver fatigue characteristics. An advanced driving state detection model is built by fusing eye features with Deep-learning using Smart Eye Pro visual tracking system, and maps human-machine gain control factors for different states to achieve dynamic allocation of driving control. On this basis, a time-varying driver-intelligent driving system dual-loop T-S fuzzy road tracking control system is constructed using vehicle longitudinal speed and gain control factors. Furthermore, a human-machine collaborative shared lateral torque controller is designed. Ultimately, the effectiveness of the proposed control strategy in adapting to various fatigue characteristics of driving behaviors is verified through Hardware-in-the-Loop (HIL) experimental validation.


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    Titel :

    Research on Human-machine Lateral Cooperative Control of Co-driving Vehicle Considering Driver Fatigue Characteristics


    Beteiligte:
    Liu, Xin (Autor:in) / Chen, Zhengfeng (Autor:in) / Cui, Guanjie (Autor:in) / Tang, Xinyu (Autor:in) / Li, Pengyuan (Autor:in) / Wu, Jian (Autor:in)


    Erscheinungsdatum :

    2023-10-27


    Format / Umfang :

    3741410 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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