Driving style identification plays a crucial role in enhancing traffic safety and reducing fuel consumption. While existing literature primarily focuses on analyzing driving styles in light-duty vehicles using simulation or questionnaire data, research on heavy-duty vehicles, despite their significant contribution to fatal accidents, remains scarce. To bridge this gap, this paper presents an attention encoder-decoder deep framework for the autonomous identification of risk driving in heavy-duty vehicles. The proposed model capitalizes on multi-source heterogeneous data collected under connected environment. High-level representations of driving style are learned through a BiLSTM-based deep architecture. Through the attention mechanism, crucial information for prediction is adaptively focused, thereby generating personalized latent semantic vectors at different time steps. The framework’s evaluation utilizes multi-source heterogeneous driving data from nearly 13,000 vehicles, conclusively demonstrating the model’s superiority over competing approaches in accurately identifying risk driving behaviors in connected heavy-duty vehicles.


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    Title :

    Attention Encoder-Decoder Network Based Autonomous Risk Driving Identification for Connected Heavy-Duty Vehicles


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Qu, Yi (editor) / Gu, Mancang (editor) / Niu, Yifeng (editor) / Fu, Wenxing (editor) / Xu, Tian (author) / Tang, Kun (author) / Guo, Tangyi (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2023 ; Nanjing, China September 09, 2023 - September 11, 2023



    Publication date :

    2024-04-27


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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