Date of Publication :8th May 2024

Abstract:Driver fatigue is a significant factor contributing to a high number of traffic incidents, with recent data highlighting its prevalence in accidents and the suffering it causes to thousandsof people. Drowsiness, responsible for nearly 30% of accidents, underscores the urgent need for technology that can detect fatigue and alert drivers in time to prevent accidents and save lives. In this study, we propose a machine learning and visual information-based approach to identify driver drowsiness, utilizing a webcam to continuously monitor the driver. This technology tracks and analyzes the driver's face and eyes, focusing on thescientifically supported correlation betweentiredness and sluggish eye closure. The model extracts the driver's face and uses the eye region to predict blink patterns, triggering an alert for the driver in case of elevated blinking rates.

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