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Title	A Comprehensive Review Of Deep Learning And Iot In Driver Drowsiness Detection For Safer Roads
ID_Doc	914
Authors	Malik R.; Vijarania M.; Malik M.
Year	2024
Published	Proceedings of the 2024 3rd Edition of IEEE Delhi Section Flagship Conference, DELCON 2024
DOI	http://dx.doi.org/10.1109/DELCON64804.2024.10866889
Abstract	This study emphasizes the significance of driver monitoring systems (DMS) for vehicle safety, particularly given their integral role in the Advanced Driver Assistance System (ADAS). It goes over the various components and levels of automation in ADAS, highlighting the importance of precise driver state monitoring for increased traffic safety. It also looks at the role of active safety modules and the possibility of improved detection accuracy and real-time feedback from IoT - based driver drowsiness detection systems. The study highlights the significance of precise measurement instruments and early warning systems while describing the signs and stages of driver drowsiness. It includes a variety of methods for detecting drowsiness, including image-based, hybrid, biological, and vehicle-based strategies. The study also examines Internet of Things (IoT) tools that are commonly used to identify driver fatigue, such as wearables, smart glasses, AI-enabled dashboard cameras, and GPS. Among the difficulties faced by IoT-based drowsiness detection systems are concerns about data reliability, power efficiency, privacy, and sensor accuracy. The conclusion highlights the importance of machine learning techniques with internet of things devices and the opportunities that 5G networks present for enhancing driver fatigue identification systems for roadways with greater safety and smart cities. © 2024 IEEE.
Author Keywords	ADAS; Deep Learning; Detection; Driver; Drowsiness; IoT; Safe Road; Vehicle

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