One of the most prevalent causes of fatal crashes that result in serious injuries or fatalities as well as major financial costs for victims, families, and society as a whole is fatigued driving. As a result of microsleeps, a fatigued driver poses a significantly greater risk to other road users than a fast driver. Scientists and firms in the automotive industry are working hard to find remedies to this challenge. In this paper, neural network-based approaches are used to identify short-term sleep and fatigue. Preventing road crashes caused by fatigued motorists may be as simple as triggering an alarm. Fatigue can be detected using a variety of techniques. The accuracy of classifying sleepiness was improved in this study by using camera-detected features of the face and a Convolutional Neural Network (CNN). As the system has been planted into portable smart device, it could be widely used for driving fatigue detection in daily life.


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    Titel :

    An Efficient Detection of Driver Tiredness and Fatigue using Deep Learning


    Beteiligte:
    Nirmala, M (Autor:in) / Angelina, Jane Rubel (Autor:in)


    Erscheinungsdatum :

    25.11.2022


    Format / Umfang :

    461978 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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