A review on the effectiveness of driver drowsiness detection system using computerised devices

Abstract

Driver drowsiness is a critical factor contributing to road accidents worldwide, with potentially devastating consequences. To mitigate this problem, numerous driver drowsiness detection systems have been developed, employing various computerized devices and technologies. The development of technologies for detecting drowsiness is a major challenge in the field of accident-avoidance systems. Since the dangers of drowsiness exist on the road, techniques need to be advanced to prevent its consequences. The most crucial aspect of this work is to develop a drowsiness detection system by tracking the driver's eyes. It is believed that signs of driver exhaustion can be detected early enough to prevent a car accident. This technology aims to enhance road safety by alerting drivers or triggering safety measures when drowsiness is detected, ultimately saving lives and preventing accidents. Detecting fatigue involves observing eye movements and blink patterns. This review aims to provide an in-depth analysis of the effectiveness of these systems in identifying and alerting drowsy drivers, ultimately enhancing road safety. The analysis of facial images is a popular research area with applications such as face recognition and human identification security systems. Furthermore, this review delves into the various methodologies and algorithms used in driver drowsiness detection, including machine learning, image analysis, and signal processing techniques. It also highlights the need for further research to address the challenges and limitations faced by these systems and suggests potential avenues for improvement.