Using the interweaving zone road section of US101 in NGSIM as a research case, the effects of various data processing methods were compared, and the characteristics of lane-changing behavior in the interweaving zone were statistically analyzed, and it was found that most of the lane-changing behavior on the detected road section of US101 occurred in the middle section. The results demonstrate that the Multi-head CNN-BiLSTM model has higher accuracy and stronger robustness than the LSTM model in recognizing lane-change intentions in the weaving zone of the highway. The research results can be used in connected autonomous vehicles and vehicle–road collaboration systems for recognition judgments.


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    Titel :

    Analysis of Lane-Change Behavior and Prediction of Lane-Change Intentions in Interweaving Areas Based on NGSIM Data


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wang, Wuhong (Herausgeber:in) / Guo, Hongwei (Herausgeber:in) / Jiang, Xiaobei (Herausgeber:in) / Shi, Jian (Herausgeber:in) / Sun, Dongxian (Herausgeber:in) / Xie, Haoyan (Autor:in) / Sun, Xiaohan (Autor:in) / Wei, Xinge (Autor:in) / Zhou, Xuan (Autor:in) / Ran, Bin (Autor:in)

    Kongress:

    International Conference on Green Intelligent Transportation System and Safety ; 2022 ; Qinghuangdao, China September 16, 2022 - September 18, 2022



    Erscheinungsdatum :

    29.09.2024


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch