Cooperative driving of human driver and automated system can effectively reduce the necessity of extremely accurate environment perception of highly automated vehicles, and enhance the robustness of decision-making and motion control. However, due to the two players’ different intentions, severe conflicts may exist during the cooperation, which often result in negative consequences on driving safety and maneuverability. This paper presents an indirect shared control method to model the situation and improve the driving performance, which focus on the affine input nonlinear vehicle dynamic system for shared controller design under the framework of non-zero sum differential game. The Nash equilibria strategy indicates the best response for the automated system, which can guide the automated controller to act more safely and comfortably. Aimed to obtain fast solution for practical application, approximate dynamic programming is utilized to find the Nash equilibria, which is represented by deep neural networks and solved iteratively. Driver-in-the-loop tests on a driving simulator were conducted to verify the performance of the proposed method under highway driving scenarios. The results show that the designed controller is able to reduce the driving workload and ensure the driving safety.


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    Title :

    Indirect Shared Control Through Non-Zero Sum Differential Game for Cooperative Automated Driving


    Contributors:
    Li, Wenyu (author) / Li, Qingkun (author) / Li, Shengbo Eben (author) / Li, Renjie (author) / Ren, Yangang (author) / Wang, Wenjun (author)


    Publication date :

    2022-09-01


    Size :

    3182547 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Indirect Shared Control of Highly Automated Vehicles for Cooperative Driving between Driver and Automation

    Li, Renjie / Li, Yanan / Li, Shengbo Eben et al. | ArXiv | 2017

    Free access