A vehicle's lane-changing behavior is affected by the surrounding environment and driver factors, which makes it difficult to identify accurately. To solve this problem, a personalized lane-changing decision model based on a Long Short-term Memory (LSTM) network is proposed. First, an unsupervised clustering method is applied to recognize three distinct driving styles; Second, by considering the interactions among the target vehicle and surrounding vehicles, a benefit function is constructed to measure these interactions and generate the lane-changing benefit values. The lane-changing gain values and feature parameters are used as model inputs to construct a personalized lane-changing decision model using LSTM. Finally, the proposed method is validated with the NGSIM dataset: the overall accuracy of the model reaches 97.8% when considering different driving styles, which proves that the proposed method can achieve personalized lane-changing decisions based on different drivers' lane-changing behaviors.


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    Title :

    Personalized lane change decision model based on long short-term memory network


    Contributors:
    Jabbar, M. A. (editor) / Lorenz, Pascal (editor) / Qi, XiaoBin (author) / Li, YanQiang (author) / Wang, Yong (author) / Zhang, DaiFeng (author) / Zhong, ZhiBang (author) / Du, Qian (author)

    Conference:

    Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024) ; 2024 ; Kuala Lumpur, Malaysia


    Published in:

    Proc. SPIE ; 13184


    Publication date :

    2024-07-05





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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