With the rapid progress of autonomous driving and advanced driver assistance systems, there are growing efforts to promote their safety in natural driving scenarios, especially for the detection of the traffic accidents. However, because of the dynamic camera motion and complex scene in driving situations, traffic accident detection is still challenging. In this work, we aim to give the ability of Traffic Accident Detection for driving systems by proposing a Self-Supervised Consistency learning framework, termed as SSC-TAD, that involves the appearance, motion, and context consistency learning. The key formulation is to find the inconsistency of video frames, object locations and the spatial relation structure of scene temporally between different frames captured by the dashcam videos. Within this field, different from the previous works which concentrate on predicting the future object locations or frames, we further focus on predicting the visual scene context in driving scenarios and detecting the traffic accident by considering the temporal frame consistency, temporal object location consistency, and the spatial-temporal relation consistency of road participants. In this work, this formulation is fulfilled by a collaborative multi-task consistency learning network and the visual scene context feature is represented by a graph convolution network. The superiority to the state-of-the-art is verified by exhaustive evaluations on two large scale datasets, i.e., the AnAn Accident Detection (A3D) dataset and DADA-2000 dataset collected recently.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios


    Contributors:
    Fang, Jianwu (author) / Qiao, Jiahuan (author) / Bai, Jie (author) / Yu, Hongkai (author) / Xue, Jianru (author)


    Publication date :

    2022-07-01


    Size :

    5696196 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Complexity of Driving Scenarios Based on Traffic Accident Data

    Dong, Xinchi / Zhang, Daowen / Mu, Yaoyao et al. | Springer Verlag | 2024


    Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior

    Rempe, Davis / Philion, Jonah / Guibas, Leonidas J. et al. | ArXiv | 2021

    Free access

    Object Detection for Self-Driving Car in Complex Traffic Scenarios

    Das Biplab / Agrawal Pooja | DOAJ | 2024

    Free access

    Driving Experience and Simulation of Accident Scenarios

    Berthelon, C. / Nachtergaele, C. / Aillerie, I. | British Library Conference Proceedings | 2008


    Simulation of accident scenarios and driving experience

    Berthelon, Catherine | Online Contents | 2007