Realtime accident prevention using drivers fatigue and drowsiness

Road accidents are one of the critical issues for the government. Detection of drowsiness of the driver is one of the key solutions in maintaining road safety. Drowsy driving is a major cause of traffic accidents. Driver fatigue is the main leading factor which causes of fatal road accidents worldwide. This demonstrates that in the transportation industry, in particular, a heavy vehicle driver is frequently exposed to hours of monotonous driving, which causes fatigue in the absence of frequent rest periods. As a result, it is critical to develop a road accident prevention system for detecting driver drowsiness, determining the level of driver inattention, and providing a warning when an impending hazard exists. This proposed model helps to reduce the no of road accidents by using the observations of the common man by optimizing the model in order to achieve the efficiency. The system also predicts the impairment of the car drivers. The system predicts whether the driver is drowsy or not at a good accuracy level using the aspect ratio of eye and Euclidean distance by analyzing the blink patterns of the eyes.