At present, although China’s traffic accident rate has decreased year by year, but people have been shocking for the huge number of casualties and property losses. Safety is an important issue in the transportation field. In the future, as the technology of Internet of vehicles becomes mature, the construction of road intelligent infrastructure follows up steadily, and the breakthrough of realizing autonomous driving, the beautiful vision of vehicle–road collaboration will be realized. At that time, the safe requirements for vehicle driving will be more stringent, and the correct driving decision is the premise of realizing safe driving. Therefore, this paper aims to provide drivers with more accurate and timely lane-changing decision intervention and construct a lane- changing decision model considering driving style. First, according to the lane-changing decision definition to divide the vehicle trajectory, and then the lane-changing decision characteristics are selected according to the vehicle interaction relationship. In this paper, the channel change decision model is constructed based on NGSIM data. Therefore, the two-step trajectory reconstruction technology is used to deal with data outliers and noise and extract relelant features, Basing on k-means clustering method, driving style calibration is achieved using following stage data. Finally, the XGBoost lane change decision model was constructed, and the necessity of adding driving style features and the superiority of XGBoost compared with RF and SVM models were verified through comparative experiments.


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    Title :

    XGBoost Lane-Changing Decision Model Considering Driving Style


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wang, Wuhong (editor) / Guo, Hongwei (editor) / Jiang, Xiaobei (editor) / Shi, Jian (editor) / Sun, Dongxian (editor) / Zhao, Yang (author) / Li, Yi (author) / Cheng, Pengle (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 :

    16 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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