Correct driving decisions are a prerequisite for safe driving. In this paper, we construct a lane-changing decision model considering driving style in order to provide drivers with more accurate and timely lane-changing decision interventions. Based on NGSIM data, two-step trajectory reconstruction technique is firstly used to deal with data outliers and noise, and then vehicle lane-changing trajectory extraction and phase classification are carried out according to the definition of lane-changing decision, and k-means clustering method is also used to realize driving style calibration and determine lane-changing decision variables based on following phase data. The final XGBoost lane-changing decision model was constructed, and the need to incorporate driving style features and the superiority of XGBoost over RF and SVM models were verified through comparative experiments.


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    Titel :

    Research on lane-changing decision model with driving style based on XGBoost


    Beteiligte:
    Shangguan, Wei (Herausgeber:in) / Wu, Jianqing (Herausgeber:in) / Zhang, Shuao (Autor:in) / Shao, Xiaoming (Autor:in) / Wang, Jiangfeng (Autor:in)

    Kongress:

    Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023) ; 2023 ; Xi'an, China


    Erschienen in:

    Proc. SPIE ; 12989


    Erscheinungsdatum :

    09.04.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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