Traffic accident anticipation in driving videos aims to provide early warning of accidents and encourage accurate decision-making. Previous research has primarily focused on the spatial temporal correlation at the object level, but it lacks some explainable clues and is susceptible to severe environmental changes. Hence we propose a method that utilizes driver attention as an auxiliary factor for traffic accident anticipation (DA-TAA) to enhance model training in this work. Specifically, driver attention provides valuable insights into key areas closely related to safe driving. DA-TAA consists of a self-attention feature extraction module, a temporal GRU module, and a driver attention-guided accident prediction module. We employ attention mechanisms to explore driver attention cues for accident prediction. We train the model using the DADA-2000 dataset, which includes annotated driver attention per frame and evaluate its performance on both the DADA-2000 and CCD datasets. Our extensive experiments demonstrate that DA-TAA outperforms state-of-the-art methods in traffic accident anticipation.


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    Title :

    Traffic Accident Anticipation via Driver Attention Auxiliary


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Qu, Yi (editor) / Gu, Mancang (editor) / Niu, Yifeng (editor) / Fu, Wenxing (editor) / Li, Lei-Lei (author) / Fang, Jianwu (author)

    Conference:

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



    Publication date :

    2024-04-23


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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