Heterogeneous vehicle platoons, consisting of a human-driven vehicle (HDV) as the leader and connected automated vehicles (CAVs) as followers, present a promising solution to address various challenges arising from fully autonomous driving. In this paper, we propose a novel LSTM-based distributed model predictive control (DMPC) platooning method. Initially, we develop and train a vehicle acceleration prediction model based on a long short-term memory (LSTM) network using real-world driving data. Subsequently, the predicted acceleration sequence of the leading HDV is integrated into the DMPC-based platoon control model for the following CAVs. To validate the effectiveness of our method, we conduct simulation experiments using real-world driving data. The results demonstrate that, with a time headway of 1 s, the maximum speed error and maximum spacing error of the heterogeneous vehicle platoon using the proposed LSTM-based DMPC are reduced by at least 5.8% and 5.9%, respectively, compared to the traditional DMPC method. Furthermore, the LSTM-based DMPC outperforms the Transformer-based DMPC method, resulting in a 1.0% reduction in maximum speed error and a 0.7% reduction in maximum spacing error. The proposed method effectively dampens oscillation caused by the leading HDV and enhances tracking accuracy.


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    Title :

    Distributed Model Predictive Control for Heterogeneous Platoon With Leading Human-Driven Vehicle Acceleration Prediction


    Contributors:
    Yang, Junru (author) / Chu, Duanfeng (author) / Yin, Jianhua (author) / Pi, Dawei (author) / Wang, Jinxiang (author) / Lu, Liping (author)


    Publication date :

    2024-05-01


    Size :

    2609913 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English