Lane change driving is a core behavior for adjusting speed and enhancing driving experience, in which drivers change lanes to find a more appropriate driving speed or a more comfortable driving environment. However, the complexity and variability of lane-changing operations, often accompanied by interactions with neighboring vehicles, exacerbate the dynamics and unpredictability of the driving environment. More importantly, inappropriate lane changing behavior has become one of the major factors in the frequency of traffic accidents and even fatal injuries, highlighting the major challenges to road traffic safety. Therefore, this paper proposes a lane-changing risk assessment method that considers the driving styles of surrounding vehicles. Firstly, the vehicle trajectory data provided in the highD dataset is selected as a sample to cluster analyze the driving styles of vehicles around the lane change. Then the lane changing risk evaluation index is established based on the headway time distance and stopping distance index, and the risk level is classified by fuzzy c-mean (FCM). Finally, the vehicle lane-changing risk is predicted using a long-short-term memory neural network (LSTM) with time series inputs, and the LSTM model considering driving style (DS-LSTM) is established. The experimental results show that the DS-LSTM model is more effective in predicting the risk of vehicle lane changing.


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    Titel :

    Predicting lane change risk for intelligent driving vehicles considering driving style


    Beteiligte:
    Chen, Hao (Herausgeber:in) / Shangguan, Wei (Herausgeber:in) / Meng, Lingyi (Autor:in) / Li, Fuhao (Autor:in) / Wang, Zhengli (Autor:in)

    Kongress:

    Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024) ; 2024 ; Xi'an, China


    Erschienen in:

    Proc. SPIE ; 13422


    Erscheinungsdatum :

    20.01.2025





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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