Smart Vision: AI-Driven Driver Fatigue Monitoring for Drivers

Over the past decade, continuous advancements in the field of computing technologies and artificial intelligence that had led to notable improvements in systems monitoring for drivers. Various study experiments have gathered actual data on driver drowsiness and algorithms that employs about the diverse artificial intelligence and feature combinations to significantly enhance real-time performance. This paper offers a current overview of driver drowsiness detection systems that have been developed in the past decade. In this research, we address the important issue of driver drowsiness detection and to improve road safety. The proposed system utilizes deep learning, image based learning, machine learning, and Convolutional Neural Networks (CNN) that accurately identifies the signs of driver fatigue. Using a comprehensive data set, the model is trained to recognize drowsiness. The developed solution contributes to advancing driver safety technology by providing a robust and real-time method for detecting and preventing drowsy driving accidents.