Abstract: Driver drowsiness is a major contributing factor in road accidents, emphasizing the need for enhanced detection measures to improve car safety. This paper describes a multi-modal fatigue detection system that uses data from an internal camera, a front camera, and vehicle factors to reliably assess driver alertness. The technology outperforms traditional methods in terms of detection accuracy by utilizing powerful machine learning algorithms. Simulation and real-world tests show considerable improvements in reliability and performance. This integrated strategy offers a promising alternative for reducing the dangers associated with driver weariness and improving overall traffic safety.

Guirrou Hamza, Mohamed Zeriab Es-Sadek and Youssef Taher, “Enhancing Road Safety: A Multi-Modal Drowsiness Detection System for Drivers” International Journal of Advanced Computer Science and Applications(IJACSA), 16(1), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160197

@article{Hamza2025,
title = {Enhancing Road Safety: A Multi-Modal Drowsiness Detection System for Drivers},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160197},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160197},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {1},
author = {Guirrou Hamza and Mohamed Zeriab Es-Sadek and Youssef Taher}
}