Intoxicated driving, sleepiness, and reckless driving are the most common causes of accidents and deaths globally, and the major causes of these accidents are usually drunken driving, drowsiness, and reckless driving. According to the United Nations, road traffic injuries have increased to 1.25 billion worldwide, making driver sleepiness detection a significant issue. A promising area for preventing countless sleep-related traffic accidents. This research provides a machine-based method for detecting tiredness. As a result of the learning algorithms, the driver is alerted in real-time. To avoid a collision The Haar Cascade method is used in the model. Along with the OpenCV library to keep track of real-time video the driving and to detect the driver's eyes the system makes use of the Eye Aspect Ratio (EAR) notion is used to detect whether or not the eyes are open.

Keywords : Drowsiness, Threshold, Eye Aspect Ratio, Drowsiness