Abstract
This review paper delves into the critical realm of drowsiness detection systems designed to mitigate the perilous consequences of driver fatigue. As instances of accidents caused by drowsy driving continue to pose significant risks on roadways, the proposed work adopts a comprehensive approach. Leveraging advanced technologies such as machine learning, computer vision, and sensor integration, the system focuses on early detection of drowsiness indicators. The algorithm, refined through iterative development, analyses facial expressions, eye movements, and physiological data for enhanced accuracy. Customizable alert mechanisms and autonomous responses, including auto-driver modes and forced parking, aim to provide timely interventions. Prospects involve multi-sensor fusion, adaptive machine learning, and integration with autonomous vehicles. As technology evolves, the goal is to create a user friendly, adaptive system that not only detects drowsiness effectively but also actively prevents potential accidents, contributing to a safer driving environment. Keywords: Drowsiness Detection, Adaptive Machine Learning, Forced Parking, Human- Machine Collaboration, Digital Object Identifier (DOI), Real- time Processing, - Friendly Design.
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