Early Identify and Notify Driver’s Drowsiness by Machine Learning

Abstract

Abstract: Drowsiness and intoxication are major contributors to car accidents, posing significant risks to road safety. The implementation of effective drowsiness detection technologies could help prevent numerous fatal accidents by alerting fatigued drivers in advance. Various techniques can be adopted to monitor driver attentiveness while driving and provide timely notifications. In the context of self-driving cars, sensors play a crucial role in identifying signs of sleepiness, anger, or extreme emotional changes in drivers. These sensors continuously monitor facial expressions and detect facial landmarks to assess the driver's state and ensure safe driving. Once such changes are detected, the system promptly assumes control of the vehicle, reducing its speed, and alerts the driver through alarms to draw attention to the situation. To enhance accuracy, the proposed system integrates with the vehicle's electronics, tracking its statistics and providing precise results. In this research, we have implemented real-time image segmentation and drowsiness detection using machine learning methodologies. Specifically, an emotion detection method based on Support Vector Machines (SVM) has been employed, utilizing facial expressions. The algorithm underwent testing under varying luminance conditions and exhibited superior performance compared to existing research, achieving an 83.25% accuracy rate in detecting facial expression changes.