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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    Title :

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


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wang, Wuhong (editor) / Guo, Hongwei (editor) / Jiang, Xiaobei (editor) / Shi, Jian (editor) / Sun, Dongxian (editor) / Xie, Haoyan (author) / Sun, Xiaohan (author) / Wei, Xinge (author) / Zhou, Xuan (author) / Ran, Bin (author)

    Conference:

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



    Publication date :

    2024-09-29


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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