Ahmed, Amira2024-03-142024-03-142023-06-22https://doi.org/10.31730/osf.io/w4ax8https://africarxiv.ubuntunet.net/handle/1/478https://doi.org/10.60763/africarxiv/436https://doi.org/10.60763/africarxiv/436https://doi.org/10.60763/africarxiv/436Drowsiness is among the important factors that cause traffic accidents; therefore, a monitoring system is necessary to detect the state of a driver’s drowsiness. Driver monitoring systems usually detect three types of information: biometric information, vehicle behavior, and the driver’s graphic information. Drowsiness detection methods based on the three types of information are discussed. A prospect for arousal level detection and estimation technology for autonomous driving is also presented. The technology will not be used to detect and estimate wakefulness for accident prevention; rather, it can be used to ensure that the driver has enough sleep to arrive comfortably at the destination. In this paper, we propose a Resnet (50) pre-trained model for driver drowsiness detection that achieves robust results and reaches 98% accuracy.Driver Detectiondeep learningResNet(50)