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Original Article
Implementation of Driver Drowsiness Detection in Intelligent Transportation Systems Using Ai and IoT
S. Nizam Ahamed1
T. Enbashakaran2
K. Saran3
K. Vinoth4
1 Assistant Professor, Department of Information Technology, PSV College of Engineering and Technology, Krishnagiri, Tamil Nadu, India. 2 3 4 UG Scholars, Department of Information Technology, PSV College of Engineering and Technology, Krishnagiri, Tamil Nadu, India.
Published Online: January-April 2026
Pages: 400-403
Cite this article
No DOIReferences
1. S. Sohail, M. Usman, and A. Khan, “Driver Drowsiness Detection Using Deep Learning and Computer Vision Techniques,” International
Journal of Advanced Computer Science and Applications, vol. 15, no. 3, pp. 145–152, 2024.
2. J. Lee, H. Kim, and S. Park, “Real-Time Driver Drowsiness Detection Using 3D Convolutional Neural Networks,” Electronics, vol. 13, no.
17, pp. 3388–3398, 2024.
3. M. Majeed, R. Ahmad, and S. Malik, “Deep Learning-Based Yawning Detection for Driver Fatigue Monitoring,” Sensors, vol. 23, no. 21, pp.
8954–8965, 2023.
4. D. Florez, J. Rodriguez, and L. Torres, “Comparative Analysis of Deep Learning Models for Driver Eye State Detection,” Applied Sciences,
vol. 13, no. 13, pp. 7849–7862, 2023.
5. A. Nasri, K. Bencherif, and M. Boudraa, “A Survey on Driver Drowsiness Detection Systems Using Computer Vision and Physiological
Signals,” IEEE Access, vol. 10, pp. 65432–65448, 2022.
Journal of Advanced Computer Science and Applications, vol. 15, no. 3, pp. 145–152, 2024.
2. J. Lee, H. Kim, and S. Park, “Real-Time Driver Drowsiness Detection Using 3D Convolutional Neural Networks,” Electronics, vol. 13, no.
17, pp. 3388–3398, 2024.
3. M. Majeed, R. Ahmad, and S. Malik, “Deep Learning-Based Yawning Detection for Driver Fatigue Monitoring,” Sensors, vol. 23, no. 21, pp.
8954–8965, 2023.
4. D. Florez, J. Rodriguez, and L. Torres, “Comparative Analysis of Deep Learning Models for Driver Eye State Detection,” Applied Sciences,
vol. 13, no. 13, pp. 7849–7862, 2023.
5. A. Nasri, K. Bencherif, and M. Boudraa, “A Survey on Driver Drowsiness Detection Systems Using Computer Vision and Physiological
Signals,” IEEE Access, vol. 10, pp. 65432–65448, 2022.
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