• Mr.Vedant Shailesh Patel
• Mr.Ashish Raghunath Patil
• Mr.Saurabh Shirish Amrutkar
• Mr.Laxmikant Satish Ghodake
• Mr.Praveen Sidhnath Kumar,Prof. Prasad. A. Lahare

Driver fatigue is a major cause of road accidents, especially for long-haul drivers. This paper surveys recent advancements in real-time driver fatigue detection systems that leverage IoT technologies and deep learning algorithms, particularly Convolutional Neural Networks (CNNs). These systems monitor critical indicators such as facial expressions, eye movements, blink rates, and head posture to detect signs of drowsiness. The data is processed in real-time, and when fatigue is detected, the system triggers alerts like audible alarms, seat vibrations, water sprinklers, or flashing parking lights to warn the driver. We evaluate various approaches in terms of accuracy, response time, and practicality in real-world conditions, including challenges like poor lighting, occlusions, and variations in driver behavior. This survey also highlights the role of IoT in enhancing system performance through continuous monitoring, data sharing, and improved alert responsiveness. In addition, we explore future directions to improve detection systems, including advancements in sensor technology, more efficient deep learning models, and strategies to reduce false positives and improve energy efficiency. The objective is to provide a comprehensive overview of the current technologies while offering insights for further research and development in enhancing road safety through fatigue detection systems.

Driver fatigue detection, Convolutional Neural Networks, Deep learning, Real-time monitoring, Alert system, IoT

"A Comprehensive Review of IoT-Enhanced Driver Fatigue Detection Systems Integrated with Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.c812-c816, October-2024, Available :http://www.jetir.org/papers/JETIR2410390.pdf

"A Comprehensive Review of IoT-Enhanced Driver Fatigue Detection Systems Integrated with Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppc812-c816, October-2024, Available at : http://www.jetir.org/papers/JETIR2410390.pdf