Published Paper Details:

Driver fatigue and drowsiness are significant contributors to road traffic accidents worldwide, posing serious threats to public safety and increasing the need for reliable driver monitoring technologies. Vision-based driver drowsiness detection systems have emerged as an effective and non-intrusive solution for monitoring driver behavior using computer vision and artificial intelligence techniques. These systems analyze visual indicators such as eye closure, blink rate, yawning behavior, head pose, and gaze direction to assess the driver's level of alertness. This paper presents a PRISMA-guided systematic review of vision-based driver drowsiness detection approaches, focusing on detection algorithms, fatigue indicators, machine learning techniques and real-time monitoring architectures. The review examines widely used techniques such as facial landmark detection, Eye Aspect Ratio (EAR), PERCLOS-based fatigue estimation, yawning detection, gaze tracking, and deep learning models. Comparative analysis of existing methods indicates that deep learning and hybrid vision-based approaches significantly enhance detection accuracy, although challenges related to illumination variation, occlusion, head pose changes, and real-time deployment remain. The study highlights current research trends and outlines future directions for developing robust and efficient driver monitoring systems for intelligent transportation environments.