With the consideration of personalized driving for automated vehicles (AVs), this paper presents a human-like decision making framework for AVs. In the modelling process, the driver model is combined with the vehicle model, which yields the integrated model for the decision-making algorithm design. Three different driving styles, i.e., aggressive, normal, and conservative, are defined for human-like driving modelling. Additionally, motion prediction algorithm is designed with model predictive control (MPC) to advance the effectiveness of the decision-making approach. Furthermore, the decision-making cost function is constructed considering drive safety, ride comfort and travel efficiency, which reflect different driving styles. Based on the decision-making cost function, a noncooperative game theoretic approach is applied to solving the decision-making issue. Finally, the proposed human-like decision making algorithm is evaluated with an overtaking scenario. Testing results indicate different driving styles cause different decision-making results, and the designed algorithm can always make safe and reasonable decisions for AVs.


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    Title :

    Human-Like Lane-Change Decision Making for Automated Driving with a Game Theoretic Approach


    Contributors:
    Hang, Peng (author) / Lv, Chen (author) / Huang, Chao (author) / Xing, Yang (author) / Hu, Zhongxu (author) / Cai, Jiacheng (author)


    Publication date :

    2020-12-18


    Size :

    747280 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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