Road accidents are skyrocketing, and traffic safety is a severe problem around the world. Many road traffic deaths are related to drivers’ unsafe behaviors. In this paper, we propose two different deep-learning models which classify the driver’s actions in a 60-second time frame into two main categories: Normal and Aggressive driving based on GPS data collected at 1 Hz, which is later preprocessed and passed to the proposed models to identify dominant driving behavior in each time frame. The models achieved an accuracy of 93.75 percent in real-world tests, which proves the efficiency of this method in driving behavior recognition.


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    Titel :

    Driving Behaviors Recognition Using Deep Neural Networks


    Beteiligte:
    Darwish, Karam (Autor:in) / Ali, Majd (Autor:in)

    Erscheinungsdatum :

    2023


    Format / Umfang :

    9-12 Pages


    Anmerkungen:

    Embedded Selforganising Systems, Vol. 10 No. 5 (2023): Applied AI Solutions on Edge Devices



    Medientyp :

    Sonstige


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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