A driver’s ability to complete routine tasks is impacted by drowsiness, which makes it a major contributing factor to car accidents, especially on highways. By detecting facial and eye movements in real-time and using geometric techniques to track eye opening and shutting, the suggested system seeks to decrease crashes brought on by driver drowsiness. DreamGuard uses cutting-edge technology including biometric sensors, computer vision, and artificial intelligence to identify driver sleepiness, providing a novel solution to this issue. When it senses sleepiness, it triggers adaptive features including haptic feedback, visual and aural alarms, and intelligent seat modifications. It continually analyses the driver’s vital signs, facial expressions, and driving style. Traffic safety is prioritised, and overall performance is improved, thanks to this system’s seamless integration with smart car technologies.


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    Titel :

    A Futuristic Approach to Driver Drowsiness Detection and Prevention Using OpenCV And HAAR Software


    Beteiligte:
    Upadhyay, Adarsh (Autor:in) / Jain, Naman (Autor:in) / Sharma, Keshav (Autor:in) / Kumar, Sandeep (Autor:in)


    Erscheinungsdatum :

    03.05.2024


    Format / Umfang :

    542815 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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