To address the low accuracy and inefficiency of current lane-change trajectory prediction methods for human-driven vehicles, this study develops a neural network lane-change trajectory prediction model with hyperparametric optimization capability using Bayesian optimization and gated recurrent units to consider the effect of lane-change intention on vehicle lane-change behavior and to predict it. The proposed model was instantiated using trajectory data of 8,721 vehicles. The results show that the overall recognition accuracy of intention recognition under the optimal input is 93.54%, and the recognition accuracy of keeping straight, left lane-change and right lane-change is 95.59%, 91.72%, and 93.30%, respectively. The root mean square errors of the predicted and actual trajectories to the left and to the right under the optimal input are 0.2582 and 0.2957, respectively. This paper demonstrates that, for the intention recognition module, the low-dimensional input enables the model to obtain high prediction accuracy, while for the trajectory prediction module, the high-latitude input enables the model to obtain a low prediction error. The developed trajectory prediction model can be used to assist in driving decision-making, path planning, and so forth.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Bayesian Global Optimization Gated Recurrent Unit Model for Human-Driven Vehicle Lane-Change Trajectory Prediction Considering Hyperparameter Optimization


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:
    Hu, Xinghua (author) / Chen, Shanzhi (author) / Zhao, Jiahao (author) / Cao, Yanshi (author) / Wang, Ran (author) / Zhang, Tingting (author) / Long, Bing (author) / Xu, Yimei (author) / Chen, Xinghui (author) / Zheng, Mintanyu (author)


    Publication date :

    2023-07-07




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    TIME TO LANE CHANGE AND COMPLETION PREDICTION BASED ON GATED RECURRENT UNIT NETWORK

    Yan, Zhanhong / Yang, Kaiming / Wang, Zheng et al. | British Library Conference Proceedings | 2019


    Time to lane change and completion prediction based on Gated Recurrent Unit Network

    Yan, Zhanhong / Yang, Kaiming / Wang, Zheng et al. | IEEE | 2019


    Vehicle Lane-Changes Trajectory Prediction Model Considering External Parameters

    Xuchuan Li / Lingkun Fan / Tao Chen et al. | DOAJ | 2021

    Free access

    Vehicle Lane-Changes Trajectory Prediction Model Considering External Parameters

    Li, Xuchuan / Fan, Lingkun / Chen, Tao et al. | BASE | 2021

    Free access