Next-Gen Driver Vigilance in Revolutionizing Road Safety by Deep Learning Approach

Driver fatigue and drowsiness are among the leading contributors to road accidents worldwide. To address this issue, the present study introduces "AI Sentinel", an advanced real-time driver monitoring system that leverages deep learning and computer vision. The system evaluates signs of fatigue by calculating the Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR), which help in detecting abnormal eye and mouth movements linked to drowsiness. Using an in-vehicle camera, facial features are continuously captured and analyzed. AI Sentinel integrates Python-based libraries such as Dlib and OpenCV to accurately track facial landmarks and trigger real-time visual or audio alerts when signs of fatigue are detected. The system achieved a 97% accuracy rate across multiple datasets and maintained reliable performance under varying conditions—including different head angles, facial accessories like masks or glasses, and diverse lighting environments. Unlike traditional approaches, this solution offers a seamless and non-intrusive method for continuous driver vigilance monitoring, ultimately aiming to reduce accidents caused by fatigue and improve road safety.