Review Article

Non-intrusive Methods used to Determine the Driver Drowsiness: Narrative Review Articles


Driver drowsiness has been one of the main causes of on-road crashes which can lead to death, physical injuries and impose significant costs on the societies. The development of non-intrusive methods to be able to detect driver drowsiness in the early stages of drowsiness is great of importance. This is an educational review and its purpose is to provide recent achievements about non-intrusive techniques used to detect driver drowsiness. Recently published related articles were searched in the scientific databases such as Web of Sciences, Scoupous, Pubmed and Google scholar. By studying the articles and extracting the important information, non-intrusive drowsiness detection methods has classified in three distinct categories: Vehicle based measures, behavioral measures and non-intrusive physiologic methods. Each of mentioned categories has its own advantages and limitations. Vehicle based methods are strongly influenced by the road geometry, whether condition and lighting. By tracking the facial expression of the driver, drowsiness can be detected. The main limitation of this method is lighting, because the cameras do not function well at night. However, physiological parameters such as electroencephalography are more reliable than vehicle and behavioral measures, the intrusive nature of these methods limit their applications. In summary, combination of the mentioned method can reliably detect the drowsiness of the driver and further studies about the efficiency of the mentioned techniques in real environments are required.

World Health Organization. Global status report on road safety: time for action. WHO press, Geneva, Switzerland, 2009.

Rau PS. Drowsy driver detection and warning system for commercial vehicle drivers: field operational test design, data analyses, and progress. 19th International Conference on Enhanced Safety of Vehicles, 6-9 June 2005; Washington, DC, USA.

Drivers Beware Getting Enough Sleep Can Save Your Life This Memorial Day; National Sleep Foundation (NSF): Arlington, VA, USA, 2010.

Thummar S, Kalariya V. A Real Time Driver Fatigue System Based On Eye Gaze Detection. Int J Eng Res Gen Sci 2015; 3(1):105-110.

Iranian Legal Medicine Organization.

Zare M, Halvani G.H, Barkhordari A, Zare A. Relations between chronic disease and crashes within professional drivers. IJOH 2010; 2(1):25-29.

Jahangiri M, Karimi A, Slamizad S, Olyaei M, Moosavi S, Amiri F. Occupational risk factors in iranian professional drivers and their impacts on traffic accidents. IJOH 2013; 5(4): 184-190.

Fletcher L, Petersson L, Zelinsky A. Driver assistance systems based on vision in and out of vehicles. Intelligent Vehicles Symposium, 9-11 June 2003; OHIO, USA.

McCartt AT, Ribner SA, Pack AI, Hammer MC. The scope and nature of the drowsy driving problem in New York State. Accid Anal Prev 1996; 28(4):511-517.

Pack AI, Pack AM, Rodgman E, Cucchiara A, Dinges DF, Schwab CW. Characteristics of crashes attributed to the driver having fallen asleep. Accid Anal Prev 1995; 27(6):769-775.

Wang J-S, Knipling RR, Goodman MJ. The role of driver inattention in crashes: New statistics from the 1995 Crashworthiness Data System. 40th annual proceedings of the Association for the Advancement of Automotive Medicine, 7-9 October 1995; Vancouver, Canada.

Bergasa LM, Nuevo J, Sotelo MA, Barea R, Lopez ME. Real-time system for monitoring driver vigilance. IEEE Tran Intell Transp Syst 2006; 7(1):63-77.

Li W, He Q-c, Fan X-m, Fei Z-m. Evaluation of driver fatigue on two channels of EEG data. Neurosci Lett 2012; 506(2):235-239.

Gharagozlou F, Saraji GN, Mazloumi A, Nahvi A, Nasrabadi AM, Foroushani AR, Kheradmand AA, Ashouri MR, Samavati M. Detecting driver mental fatigue based on EEG alpha power changes during simulated driving. Iran J Public Health 2015; 44(12):1693-1700.

Patel M, Lal S, Kavanagh D, Rossiter P. Applying neural network analysis on heart rate variability data to assess driver fatigue. Expert Syst Appl 2011; 38(6):7235-7242.

Fan X, Yin B-c, SUN Y-f. Yawning detection based on gabor wavelets and LDA. J Beijing Univ Technol 2009; 35(3):409-413.

Benedetto S, Pedrotti M, Minin L, Baccino T, Re A, Montanari R. Driver workload and eye blink duration. Transp Res Part F Traffic Psychol Behav 2011; 14(3):199-208.

Papantoniou P, Papadimitriou E, Yannis G. Assessment of driving simulator studies on driver distraction. Adv Transp Stud 2015; (35)129-144.

Marquart G, Cabrall C, de Winter J. Review of eye-related measures of drivers’ mental workload. Procedia Manuf 2015; 3:2854- 2861.

Poursadeghiyan M, Mazloumi A, Saraji G N, Niknezhad A, Akbarzadeh A, Ebrahimi M H. Determination the Levels of Subjective and Observer Rating of Drowsiness and Their Associations with Facial Dynamic Changes. Iran J Public Health 2017; 46(1):93-102.

Karchani M, Mazloumi A, NaslSaraji G, Akbarzadeh A, Niknezhad A, Ebrahimi MH, Raei M, Khandan M. Association of Subjective and Interpretive Drowsiness with Facial Dynamic Changes In Simulator Driving. J Res Health Sci 2015; 15(4): 250-255.

Fukuda K, Stern JA, Brown TB, Russo MB. Cognition, blinks, eye-movements, and pupillary movements during performance of a running memory task. Aviat Space Environ Med 2005; 76(7): 75-85.

Stern JA, Boyer D, Schroeder D. Blink rate: a possible measure of fatigue. Hum factors 1994; 36(2):285-297.

Recarte MÁ, Pérez E, Conchillo Á, Nunes LM. Mental workload and visual impairment: Differences between pupil, blink, and subjective rating. Span J Psychol 2008; 11(02):374-385.

Heger R. Driving behavior and driver mental workload as criteria of highway geometric design quality. Report number: 0097-8515. January 1998.

Friedrichs F, Yang B. Camera-based drowsiness reference for driver state classification under real driving conditions. Intelligent Vehicles Symposium (IV), 21-24 June 2010; University of California, San Diego, CA, USA.

Palinko O, Kun AL, Shyrokov A, Heeman P. Estimating cognitive load using remote eye tracking in a driving simulator. Proceedings of the 2010 symposium on eye-tracking research & applications, 22 – 24 March 2010; New York, USA.

Meshram P, Auti N, Agrawal H. Monitoring Driver Head Postures to Control Risks of Accidents. Procedia Comput Sci 2015; 50:617-622.

Teyeb I, Jemai O, Zaied M, Amar CB. A drowsy driver detection system based on a new method of head posture estimation. International Conference on Intelligent Data Engineering and Automated Learning, 10-12 September 2014; Salamanca, Spain.

Teyeb I, Jemai O, Zaied M, ben Amar C. A multi-level system design for vigilance measurement based on head posture estimation and eyes blinking. Eighth International Conference on Machine Vision, 8 December 2015; Barcelona, Spain.

Ji Q, Yang X. Real-time eye, gaze, and face pose tracking for monitoring driver vigilance. Real-Time Imaging. 2002; 8(5):357-377.

Batista J. A drowsiness and point of attention monitoring system for driver vigilance. Intelligent Transportation Systems Conference, 30 September– 3 October 2007; WA, USA.

Karchani M, Mazloumi A, Saraji GN, Nahvi A, Haghighi KS, Abadi BM, Foroshani AR, Niknezhad A. The Steps of proposed drowsiness detection system design based on image processing in simulator driving. Int J Basic Sci Appl Res 2015; 9(6): 878-887.

Zhou M, Lin H, Yu J, Young SS. Hybrid sensing face detection and recognition. Applied Imagery Pattern Recognition Workshop (AIPR), 3-15 October 2015; Washington, DC, USA.

Luczak S, Oleksiuk W, Bodnicki M. Sensing tilt with MEMS accelerometers. IEEE Sens J 2006; 6(6):1669-1675.

Leavitt J, Sideris A, Bobrow JE. High bandwidth tilts measurement using low-cost sensors. IEEE ASME Trans Mechatron 2006; 11(3):320-327.

Lawoyin S, Fei D-Y, Bai O. Accelerometer-based steering-wheel movement monitoring for drowsy-driving detection. P I Mech Eng D-J Aut 2015; 229(2):163-173.

Fairclough SH, Graham R. Impairment of driving performance caused by sleep deprivation or alcohol: a comparative study. Hum Factors 1999; 41(1):118-128.

Vural E. Video based detection of driver fatigue PhD thesis, University of Sabanci, Istanbul, Turkey, 2009.

Simons R, Martens M, Ramaekers J, Krul A, Klöpping-Ketelaars I, Skopp G. Effects of dexamphetamine with and without alcohol on simulated driving. Psychopharmacology 2012; 222(3):391-399.

Das D, Zhou S, Lee JD. Differentiating alcohol-induced driving behavior using steering wheel signals. IEEE Trans Intell Transp Syst 2012; 13(3):1355-1368.

Sahayadhas A, Sundaraj K, Murugappan M. Detecting driver drowsiness based on sensors: a review. Sensors 2012; 12(12):16937-53.

Gharagozlou F, Mazloumi A, Nasl Saraji J, Nahvi A, Motie Nasrabadi A. P25: Driver Cognitive Fatigue Detection Based on Changes in EEG Frequency Bands in Non-Professional Drivers during a Simulated Driving Task. The 2th International Neurotrauma Congress & the 4the International Roads Safety Congress, 18-20 February 2015; Tehran, Iran.

Noori SMR, Mikaeili M. Driving drowsiness detection using fusion of electroencephalography, electrooculography, and driving quality signals. J Med Signals Sens 2016; 6(1):39-46.

Lee B-G, Chung W-Y. Multi-classifier for highly reliable driver drowsiness detection in Android platform. Biomed Eng 2012; 24(02):147-154.

Mizuno A, Okumura H, Matsumura M. Development of neckband mounted active bio-electrodes for non-restraint lead method of ECG R wave. 4th European Conference of the International Federation for Medical and Biological Engineering, 23–27 November 2008; Antwerp, Belgium.

Yu X. Real-time nonintrusive detection of driver drowsiness. University of Minnesota Center for Transportation Studies CTS 09-15. May 2009.

Cheng B, Zhang W, Lin Y, Feng R, Zhang X. Driver drowsiness detection based on multisource information. Hum Factors Ergon Manuf 2012; 22(5):450-467.

Cyganek B, Gruszczyński S. Hybrid computer vision system for drivers' eye recognition and fatigue monitoring. Neurocomputing 2014; 126:78-94.

Lee B-G, Jung S-J, Chung W-Y. Real-time physiological and vision monitoring of vehicle driver for non-intrusive drowsiness detection. IET Commun 2011; 5(17):2461-2469.

IssueVol 8 No 4 (2016) QRcode
SectionReview Article(s)
Driver Drowsiness Detection Non-Intrusive Eye Detection Face Detection Yawn detection

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How to Cite
OMIDI F, NASL SARAJI G. Non-intrusive Methods used to Determine the Driver Drowsiness: Narrative Review Articles. Int J Occup Hyg. 2017;8(4):186-191.