Monitoring System for Vehicle Drivers Using AI and Machine Learning

Abstract

Road accidents, which have become more common in recent years, are significantly caused by driver fatigue and drowsiness. To address this problem, a driver monitoring system using machine learning is proposed in this research paper. A Convolutional Neural Network (CNN) is used to classify the state of the eyes and mouth for driver fatigue detection. The  Eye Aspect Ratio (E.A.R.) and Mouth Aspect Ratio (M.A.R.) are calculated to detect drowsiness. The system uses machine learning algorithms to identify facial features and alerts the driver with a buzzer when drowsiness is detected. Blink frequency and yawning are key indicators of driver fatigue. OpenCV and Dlib face detector model techniques are used to develop the proposed system. The system is camera-based, allowing for the monitoring of driver attention throughout the journey. The system was tested using a dataset of videos and images of drivers in various driving situations. The results of the experiments show that the proposed system is highly effective at detecting driver fatigue and drowsiness.