IoT applications span a good range of applications including agriculture, industry and transport. Although transport system has changed people's lives and made daily activities easier, it has also been connected to negative outcomes like road accidents. The majority of road accidents occur as a result of a driver's drowsiness. Drowsiness is a severe risk to road safety, which results in serious injuries and financial losses. Drowsiness detection in drivers is one among the important applications of IoT that helps in saving drivers and passengers from road accidents by warning the drivers at the right time. Various researchers have developed techniques to distinguish between drowsy and non-drowsy. In this paper, we analyze the different methods to detect drowsiness among drivers. The three different methods are (i) Behavioral methods, (ii) Biological methods and (iii) Vehicular features-based methods. Behavioral methods use driver’s facial characteristics for detecting drowsiness. Biological methods use driver’s physical characteristics, whereas vehicular feature-based methods use vehicular features to detect drowsiness in driver. In this research review, we have explained and compared each of these methods in detail and also discussed their advantages and disadvantages.
Driver Drowsiness Detection System Based on Behavioral Method, Biological Method and Vehicular Feature-Based Method—A Review
Smart Innovation, Systems and Technologies
International Conference on Intelligent Systems and Sustainable Computing ; 2022 December 16, 2022 - December 17, 2022
2023-10-03
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Internet of things , Drowsiness detection system , Behavioral method , Biological method , Vehicular feature-based method Engineering , Computational Intelligence , Artificial Intelligence , Cyber-physical systems, IoT , Professional Computing , Statistics, general , Signal, Image and Speech Processing
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