The main reason for road accidents is the driver's drowsiness which leads to a considerable number of car crashes, injuries, lots of fatalities, and significant economic losses. Driver's drowsiness is represented as a state which varies between sleep and wakefulness, that decreases cognitive skills and impacts the capability of performing the task of driving. This serious issue needs to develop an effective vigilance monitoring system capable of decreasing accidents by alerting the driver under various bad driving situations. For detecting drowsiness, vehicle-based methods (such as estimating the level of drowsiness depending on the movements of the steering wheel), behavioral-based methods (detecting the driver visual features using various resources such as facial expressions, eye movements, head movements, etc.), and physiologic-based methods (detecting the earlier stages of driver's drowsiness depending on physiological signals) can be utilized. This paper is focused on the designing and implementation of a driver assistance system which includes a driver's monitoring and alarming by using intrusive acquisition methods, called Electrooculography (EOG) signals. An embedded system based on ATmega2560 microcontroller on the Arduino board has been used to implement the EOG signal acquisition circuit. The developed system used several measurements to extract the features from EOG signals which makes it very sensitive to detect the driver's drowsiness. Furthermore, K Nearest Neighbors classifier (KNN) is used to give good accuracies. This system creates a low-cost device capable of quickly alerting the driver to ensure their safety. The experimental results show the efficiency and reliability of the proposed driver assistance system.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Driver's Drowsiness Monitoring and Alarming Auto-System Based on EOG Signals


    Beteiligte:
    Hayawi, Ali Amer (Autor:in) / Waleed, Jumana (Autor:in)


    Erscheinungsdatum :

    01.08.2019


    Format / Umfang :

    880104 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Driver’s Drowsiness Detection Using SpO2

    Sugantha Priyadharshini, P. / Jayakiruba, N. / Janani, A. D. et al. | Springer Verlag | 2023



    METHOD FOR RECOGNIZING DRIVER'S DROWSINESS AND APPARATUS FOR RECOGNIZING DROWSINESS

    BAE YOU SUK / KIM HYUN SUK / KIM JUN O et al. | Europäisches Patentamt | 2017

    Freier Zugriff

    Monitoring Driver’s Drowsiness Status at Night Based on Computer Vision

    Valsan A, Vidhu / Mathai, Paul P / Babu, Ierin | IEEE | 2021


    Real-Time Based Driver's Drowsiness and Fatigue Detection System

    Srivastava, Ashish / Bansal, Shreya / Sehgal, Satbir S | IEEE | 2022