Detecting Driver Drowsiness Based on Sensors: A Review

By Arun Sahayadhas, Kenneth Sundaraj, Murugappan Murugappan. Italian National Conference on Sensors, 2012

760 citations recorded by the source corpus.

In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.

Subfields: Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/f4c9585eee67c861cfbd43dbcf202a740b3940e8

How researchers see this paper

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In Experimental and Cognitive Psychology

1. Wei-Long Zheng
2. Bao-Liang Lu
3. Wei Liu
4. Zhen Cui
5. Wenming Zheng
6. Jinpeng Li
7. Huiguang He
8. Jielin Qiu

In Social Psychology

1. Bao-Liang Lu
2. Wei-Long Zheng

Citations in the corpus

• Real-Time Eye Blink Detection using Facial Landmarks (2016, background): “driver drowsiness [5, 13], in systems that warn a computer user staring at the screen without blinking for a long time to prevent the dry eye and the computer vision syndromes [17, 7, 8], in humancomputer interfaces that ease communication for disabled people [15], or for anti-spoofing protection in”
• Driver Behavior Analysis for Safe Driving: A Survey (2015, background): “Some car companies such as Nissan and Renault are using SWM, but it works for limited cases [76].”
• A multimodal approach to estimating vigilance using EEG and forehead EOG (2016, background): “Various approaches for estimating vigilance levels have been proposed in the literature (Ji, Zhu & Lan 2004, Dong, Hu, Uchimura & Murayama 2011, Sahayadhas, Sundaraj & Murugappan 2012).”