Driver Drowsiness Detection Using CNN

The condition and emotional state of a driver can be evaluated using advanced machine learning algorithms, including neural networks, contributing to enhanced road safety. Leveraging artificial intelligence, these systems can autonomously learn and adapt without explicit programming. Driving health and alertness can be assessed through bio-indicators, driving behaviors, and facial expressions. This paper presents a comprehensive review of recent advancements in driver drowsiness detection and alert systems, highlighting the application of machine learning techniques such as YOLO, CNN, and OpenCV. With detection accuracy exceeding 80% and precision recall and F1 score of 97, 98 and 98 % accordingly, this application on Neural Networks can help increase road safety.