To anticipate driving intentions in advance, the natural driving trajectory is utilized for predicting driver lane change intentions based on driving style analysis. Initially, Tracker is employed to extract natural driving trajectories from the expressway merging area. Subsequently, K-means++, Spectral Clustering, and DBSCAN are introduced to determine the clustering model that exhibits the best performance in analyzing driving styles. Lastly, the XGBoost, LightGBM, and stacking models are proposed to identify driver lane change intentions and analyze the impact of driving style information on the model. The results indicate that the best outcomes are achieved when K-means++ is used to categorize driving styles into conservative, normal, and aggressive categories. The stacking model exhibits the highest accuracy, while LightGBM demonstrates the fastest training speed. Considering driving style information enhances the model’s performance, yielding an accuracy of over 85% within 1.5 s prior to the lane change initiation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Lane Change Intention Recognition Based on Natural Driving


    Contributors:
    Hao, Yanjun (author) / Dong, Hongxia (author) / Zhao, Xiaojin (author) / Zhang, Zhishun (author) / Xu, Ting (author)

    Conference:

    24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China


    Published in:

    CICTP 2024 ; 2359-2369


    Publication date :

    2024-12-11




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    DRIVING INTENTION RECOGNITION AND LANE CHANGE PREDICTION ON THE HIGHWAY

    Han, Teawon / Jing, Junbo / Özgüner, Ümit | British Library Conference Proceedings | 2019


    Driving Intention Recognition and Lane Change Prediction on the Highway

    Han, Teawon / Jing, Junbo / Ozguner, Umit | IEEE | 2019


    Driving Intention Recognition and Lane Change Prediction on the Highway

    Han, Teawon / Jing, Junbo / Ozguner, Umit | ArXiv | 2019

    Free access


    Driver intention recognition method based on comprehensive lane-change environment assessment

    Ding, Jieyun / Dang, Ruina / Wang, Jianqiang et al. | IEEE | 2014