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.


    Access

    Download


    Export, share and cite



    Title :

    Driving Behaviors Recognition Using Deep Neural Networks


    Contributors:
    Darwish, Karam (author) / Ali, Majd (author)

    Publication date :

    2023


    Size :

    9-12 Pages


    Remarks:

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



    Type of media :

    Miscellaneous


    Type of material :

    Electronic Resource


    Language :

    English





    Recognition Method of Drinking-driving Behaviors Based on PCA and RBF Neural Network

    Yifan Sun / Jinglei Zhang / Xiaoyuan Wang et al. | DOAJ | 2018

    Free access

    HEADLIGHT RANGE ESTIMATION FOR AUTONOMOUS DRIVING USING DEEP NEURAL NETWORKS

    Mayr, Jakob / Giracoglu, Can / Unger, Christian et al. | British Library Conference Proceedings | 2019


    Headlight Range Estimation for Autonomous Driving using Deep Neural Networks

    Mayr, Jakob / Giracoglu, Can / Unger, Christian et al. | IEEE | 2019


    Pattern recognition on aerospace images using deep neural networks

    Saetchnikov, Ivan / Skakun, Victor / Tcherniavskaia, Elina | IEEE | 2020