With the growth of vehicular computing capacity, there is an increasing demand for real-time data processing. However, data is sometimes not optimal for purposes such as storage or training. To address this issue, we propose a solution to enhance vehicle safety by generating abnormal image data. We then leverage machine learning algorithms to detect and classify these anomalies while vehicles are in operation. An edge-based anomaly detection approach will be applied to prevent accidents and enhance the safety of connected vehicles.


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    Titel :

    Poster: Enhancing Autonomous Vehicles Safety Through Edge-Based Anomaly Detection


    Beteiligte:
    Wang, Qiren (Autor:in) / Feng, Ruijie (Autor:in) / Shi, Weisong (Autor:in)


    Erscheinungsdatum :

    2023-12-06


    Format / Umfang :

    479053 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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