Driving Alertness System Using Deep Learning

Abstract

Abstract: Driver fatigue, a major contributor to road accidents, poses a significant challenge to road safety. Numerous fatal collisions could be prevented by promptly alerting drivers experiencing drowsiness. Several drowsiness detection methods have been developed to monitor driver alertness while driving and issue warnings when attention lapses. These systems employ various techniques to assess drowsiness levels, including extracting key features from facial expressions such as yawning, eye closure, and head movements. Evaluating both the driver's physiological state and vehicle behavior is crucial for identifying signs of drowsiness. This research delves into a comprehensive evaluation of prevalent classification methodologies and a thorough examination of contemporary driver drowsiness detection technologies. The study initially categorizes existing techniques into three broad groups: physiological, vehicular, and behavioral parameter-based approaches. It then provides an extensive review of recognized supervised learning algorithms employed for drowsiness detection. Furthermore, the examination conducts a comparative analysis of the advantages and limitations of various methods.