Autonomous driving vehicle will come reality more and more possible along with prosperity of relevant technologies. However, autonomous driving vehicles and artificial driving vehicles will coexist for a long time until full connected and autonomous traffic environment achieved. To modeling lane-changing decision of autonomous driving vehicles, which can be regard as non-cooperative static game under complete information, we establish the game theory-based lane-changing model with combined driving style (GLCD model). Then we conduct a series of simulation experiments to evaluate indicators of the GLCD model, which include the number of lane-changing, the number of accidents, the number of vehicles passes and, the average passing time, compared with the cellular automata model, also compared with the general game theory-based lane-changing model. Results show that the developed model (GLCD model) can improve the efficiency of vehicle driving and ensure stability on the road under the premise of ensuring safety. Furthermore, this model can provide a feasible vehicle lane-change decision in a mixed traffic flow environment in the future.


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    Title :

    Lane-changing decision model for autonomous vehicle under mixed traffic environment


    Contributors:
    Xu, Wanhui (author) / Liu, Yugang (author) / Yi, Hongbo (author) / Liu, Gang (author)

    Conference:

    Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022) ; 2022 ; Guangzhou,China


    Published in:

    Proc. SPIE ; 12591


    Publication date :

    2023-02-16





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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