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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    Titel :

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


    Beteiligte:
    Jabbar, M. A. (Herausgeber:in) / Lorenz, Pascal (Herausgeber:in) / Qi, XiaoBin (Autor:in) / Li, YanQiang (Autor:in) / Wang, Yong (Autor:in) / Zhang, DaiFeng (Autor:in) / Zhong, ZhiBang (Autor:in) / Du, Qian (Autor:in)

    Kongress:

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


    Erschienen in:

    Proc. SPIE ; 13184


    Erscheinungsdatum :

    05.07.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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