Key points are not available for this paper at this time.
Abstract: Driver drowsiness is a significant factor in road accidents, necessitating the development of effective detection and alert systems to mitigate this risk. This paper provides a comprehensive review of driver drowsiness detection and alert systems, examining the technologies, methodologies, and challenges associated with these systems. Our goal is to provide an interface where the program can automatically detect the driver's drowsiness and detect it in the event of an accident by using the image of a person captured by the webcam and examining how this information can be used to improve driving safety can be used. . a vehicle safety project that helps prevent accidents caused by the driver's sleep. Basically, you're collecting a human image from the webcam and exploring how that information could be used to improve driving safety. Collect images from the live webcam stream and apply machine learning algorithm to the image and recognize the drowsy driver or not. Despite advancements, challenges remain in achieving high accuracy and minimizing false alarms. Future directions include improving sensor technology, enhancing algorithm robustness, and addressing user acceptance issues. Overall, driver drowsiness detection and alert systems play a crucial role in enhancing road safety and warrant continued research and development efforts.
Patle et al. (2024) studied this question.