The trajectory prediction is significant for the driving safety of intelligent and connected vehicles. To accurately predict the vehicle trajectory, a hybrid method combining physic-based and data-based methods is proposed for intelligent and connected vehicles. The proposed method applied the physic-based method to represent vehicle kinematics. Then, the error of the physic-based method, which is the unmodeled features, is modeled with the data-based deep learning method using Encoder-Decoder Long short-term memory (LSTM). The proposed method is trained and evaluated by an actual vehicle dataset. When the prediction horizon is 3s, compared with the physic-based method, the longitudinal error, lateral error, and yaw angle error decreased by 93.9%, 86.6%, and 76.0%, respectively. Results show that the proposed method improves the trajectory prediction accuracy of autonomous and connected vehicles.


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    Title :

    Trajectory prediction method using deep learning for intelligent and connected vehicles


    Contributors:
    Qie, Tianqi (author) / Wang, Weida (author) / Yang, Chao (author) / Li, Ying (author) / Zhang, Yuhang (author) / Liu, Wenjie (author)


    Publication date :

    2023-05-08


    Size :

    432737 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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