The uncertainties of driver's behavior seriously affect road safety and bring significant challenges to the human-machine cooperative control. This paper proposes a human-machine shared control framework considering driver's time-varying characteristics to improve the co-driving cooperation performance. Firstly, the driving intention is introduced to describe the driver's involvement level through using Gauss-Bernoulli restricted Boltzmann machine method. And the index of driving ability is proposed to evaluate driver skills based on path-tracking errors. Then, a novel human-machine authority allocation strategy is designed by combining the two driving behavior characteristics and used to construct the driver-vehicle interaction system. Subsequently, a T-S fuzzy robust state-feedback shared control system is developed considering time-varying driver behaviors and vehicle states. Finally, the proposed shared steering system is validated by the driver-in-the-loop test bench. The results show that the proposed control method can reduce human-machine conflicts and has obvious superiority in improving performance of driving comfort, path tracking, and vehicle stability for the co-driving vehicles.


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    Titel :

    A Human-Machine Shared Control Framework Considering Time-Varying Driver Characteristics


    Beteiligte:
    Fang, Zhenwu (Autor:in) / Wang, Jinxiang (Autor:in) / Wang, Zejiang (Autor:in) / Liang, Jinhao (Autor:in) / Liu, Yahui (Autor:in) / Yin, Guodong (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.07.2023


    Format / Umfang :

    4878113 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch