State-of-the-Art in Driver’s Drowsiness Detection: A Comprehensive Survey

Abstract

Recent developments in computing technology and advancements in artificial intelligence have led to major improvements in driver monitoring systems. These enhancements are crucial given the substantial risk posed by fatigue or drowsiness on roads, leading to numerous accidents and impacting overall road safety. Timely detection and alert mechanisms for fatigued drivers can prevent many such accidents. Various methods have been formulated to monitor driver drowsiness during operation and alert drivers when their attention wanes. These methods utilize facial expressions like yawning, eye closures and head movements to gauge the level of drowsiness, in addition to analyzing biological markers of fatigue in the driver's body and monitoring vehicle behavior. This study delves into an in-depth analysis of existing driver drowsiness detection methods, focusing on widely used classification techniques. The paper classifies these methods into three primary categories: behavioral, vehicular and physiological based techniques. This study thoroughly investigates advanced methods for detecting driver fatigue, utilizing multiple machine learning and deep learning capabilities and a comparative study of these methods is presented. The research includes a detailed analysis of approaches to detect driver fatigue through facial landmark detection, employing established algorithms rooted in artificial neural networks and computer vision principles. Furthermore, the paper discusses potential advancements in driver fatigue detection technologies, providing valuable guidance for professionals and researchers striving to improve road safety through effective fatigue detection systems.