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Detecting Driver Drowsiness Based on Sensors: A Review

Sensors · 2012 · Vol. 12(12) · pp. 16937–16953
Arun SahayadhasKenneth SundarajM. Murugappan

Abstract

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-intrusive 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.

MeSH terms

Accidents, TrafficAutomobile DrivingHumansSleep Stages
Citations
745
FWCI
13.54
field-weighted impact
References
80
Percentile
99%
vs. same field & year
Citations per year
Cited by
Driver Fatigue Detection Systems: A Review
IEEE Transactions on Intelligent Transportation Systems · 2018 · 498 citations
References
Fatigue, sleep restriction and driving performance
Accident Analysis & Prevention · 2005 · 371 citations
Monotony of road environment and driver fatigue: a simulator study
Accident Analysis & Prevention · 2003 · 668 citations
Real-Time System for Monitoring Driver Vigilance
IEEE Transactions on Intelligent Transportation Systems · 2006 · 700 citations
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