Modern cars can detect unsafe driving by comparing the observed behavior of the subject vehicle (i.e., rear vehicles) with normal driving. However, normal driving does not have a standard definition. It changes depending on the situation. In this work, we address this problem and propose edge-assisted unsafe driving detection. In our proposal, instead of learning normal driving, the edge infers the most common unsafe driving patterns. It then shares this knowl-edge with cars. Cars look for such patterns to detect unsafe driving. Analysis of real-world traffic data shows that edge-assisted unsafe driving detection could detect unsafe behavior of subject vehicles with 90% accuracy.


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    Titel :

    Poster: Edge-Assisted Unsafe Driving Detection


    Beteiligte:
    Ucar, Seyhan (Autor:in) / Sisbot, E. Akin (Autor:in) / Oguchi, Kentaro (Autor:in)


    Erscheinungsdatum :

    06.12.2023


    Format / Umfang :

    339456 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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