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Driver drowsiness is critical toroad accidents and fatalities worldwide. Various systems utilizing image processing, computer vision, and machine learning techniques have been proposed to address this. This paper consolidates the findings from few relevant studies focusing on drowsiness detection in drivers. The proposed systems aim to detect signs of drowsiness, such as eye closure and facial expressions, and issue timely alerts to prevent accidents. By analyzing these studies, thispaper provides insights into the methodologies, challenges, and advancements in drowsiness detection technology, paving the way for more robust and effective systems in the future.
Viraktamath et al. (Fri,) studied this question.