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Detecting Driver Drowsiness Based on Sensors: A Review

AI-Rehab Research Group, Universiti Malaysia Perlis (UniMAP), Kampus Pauh Putra, 02600 Arau, Perlis, Malaysia
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Sensors 2012, 12(12), 16937-16953; https://doi.org/10.3390/s121216937
Received: 27 September 2012 / Revised: 22 November 2012 / Accepted: 2 December 2012 / Published: 7 December 2012
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: driver drowsiness detection; transportation safety; hybrid measures; driver fatigue; artificial intelligence techniques; sensor fusion driver drowsiness detection; transportation safety; hybrid measures; driver fatigue; artificial intelligence techniques; sensor fusion
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MDPI and ACS Style

Sahayadhas, A.; Sundaraj, K.; Murugappan, M. Detecting Driver Drowsiness Based on Sensors: A Review. Sensors 2012, 12, 16937-16953.

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