• Abhinandan Raj
• Angel Pati
• Lalit Mohan

Driver drowsiness is a critical factor contributing to road accidents, making the development of effective detection systems essential for enhancing road safety. This research focuses on designing a real-time drowsiness detection system leveraging advanced technologies such as computer vision and machine learning. The proposed system monitors drivers' facial behaviors, including eye closure, blinking frequency, and yawning, to assess fatigue levels accurately. Utilizing a Convolutional Neural Network (CNN) model, trained on a diverse dataset of facial expressions, the system achieves high precision in identifying drowsy states. The experimental results demonstrate the system's effectiveness, achieving an accuracy rate of over 90% in various lighting and environmental conditions. By addressing existing challenges such as false positives and environmental adaptability, this study contributes a robust and scalable solution to the global issue of driver fatigue. The implementation of such systems in vehicles promises to significantly reduce road accidents and save lives.

Drowsiness Detection, Driver Fatigue, Machine Learning, Road Safety, Preventive Systems.

"Drivers Drowsiness Detection ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.a403-a410, February-2025, Available :http://www.jetir.org/papers/JETIR2502050.pdf

"Drivers Drowsiness Detection ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. ppa403-a410, February-2025, Available at : http://www.jetir.org/papers/JETIR2502050.pdf