Driver behavior has become an essential consideration throughout the current smart transportation system. Most drivers are unfamiliar with this new technology and very ignorant of their driving style, which leads to problems like a violation of pedestrian safety. Driver behavior analysis (DBA) is intended to improve passengers’ safety from harsh driving by analyzing the behavior of selected drivers while driving. Therefore, this paper aims to develop a model to detect aggressive driving to minimize traffic safety violations. This paper presents a method using SimpleRNN, LSTM, and GRU individually to detect drivers’ actions from statistical numeric data where LSTM performs better. A public dataset that uses numeric sequential data to predict driver's behavioral conditions. This paper also compares the proposed DBA algorithm with previously used deep learning and machine learning models to detect driver actions by accuracy and precision. After many epochs, the paper concludes that the LSTM model yields greater success than most other models and achieves 0.961 accuracies.


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    Titel :

    Driver Behavior Analysis Based on Numerical Data Using Deep Neural Networks


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Saraswat, Mukesh (Herausgeber:in) / Roy, Sarbani (Herausgeber:in) / Chowdhury, Chandreyee (Herausgeber:in) / Gandomi, Amir H. (Herausgeber:in) / Lima, Aklima Akter (Autor:in) / Das, Sujoy Chandra (Autor:in) / Shahiduzzaman, Md. (Autor:in)


    Erscheinungsdatum :

    2021-11-23


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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