Driver fatigue is a major contributor to road accidents, necessitating the integration of advanced technologies for improved road safety. This research presents DrowseGuard, a DeepAlert Driver Vigilance System designed for seamless incorporation into Advanced Driver Assistance Systems (ADAS) within vehicles. Utilizing a sophisticated deep learning model and mediapipe framework, DrowseGuard offers precise eye monitoring, thereby enhancing fatigue detection accuracy. The system surpasses conventional methods by analyzing dynamic factors such as sudden speed spikes and unexpected turns. Real-time alerts issued by DrowseGuard provide a comprehensive solution for enhancing road safety through ADAS infrastructure. The synergistic integration of the Deep Learning technology and ADAS enhances accuracy, enabling early identification of signs of driver fatigue. Unlike conventional development board implementations, DrowseGuard's direct integration ensures practicality, scalability, and applicability across diverse vehicle platforms. This research represents a significant advancement in driver-assistance technologies, aiming to elevate overall safety standards in modern vehicles. The motivation lies in addressing the escalating concern of road accidents attributed to driver fatigue, while the objective is to enhance road safety through the integration of DrowseGuard into ADAS systems.


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    Title :

    DrowseGuard – DeepAlert Driver Vigilance System


    Contributors:


    Publication date :

    2024-07-12


    Size :

    1601591 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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