Skip to main content
SpringerLink
Log in

Drowsy and Fatigued Driving Problem Significance and Detection Based on Driver Control Functions

  • Reference work entry
Handbook of Intelligent Vehicles

Abstract

Drowsy and fatigue driving is a major transportation safety concern and is responsible for thousands of accidents and numerous fatalities every year. The resulting harms of drowsy/fatigue driving could be even higher among commercial vehicles. Drowsy driving crashes are usually of high severity due to the drivers’ significant loss of control, often leading to unpredicted vehicle trajectory and no braking response. Reliable safety systems are needed to mitigate these crashes. The most important challenge is to detect the driver’s condition sufficiently early, prior to the onset of sleep, to avoid collisions.

Various detection methods have been proposed by researchers and a few systems are available in the commercial market. In general, drowsiness detection methods fall into two major categories of monitoring physiological and physical conditions of the drivers and monitoring vehicle-related variables based on driver control functions that correlate with the driver’s level of drowsiness. Each method has its advantages and shortcomings. A reliable detection method needs to be integrated with a safety system which may include advisory warning, semi-control, or full control of vehicle, i.e., braking and steering to achieve safe conditions. The type and intensity of warning or control should also be carefully selected and are discussed in another chapter.

This chapter first reviews the statistical significance of the crash data due to drowsiness and fatigue conditions. Then, the issues concerning various detection methods are discussed. Detection systems based on driver control functions are mainly discussed in this chapter. The concepts and approaches presented in this section are from a comprehensive literature review including the author’s past research; they can guide the development of safety systems for a passenger or commercial vehicles.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 849.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Akerstedt T, Kecklund G (1994) Work hours, sleepiness and accidents. Karolinska Institute, Stockholm:1994, 104 (Stress Research Report No 248)

    Google Scholar 

  • Andreeva E, Arabi P, Philiastides M, Mohajer K, Emami M (2004) Driver drowsiness detection using multi-modal sensor fusion. Proc SPIE 5434:380–390

    Article  Google Scholar 

  • Artaud P, Planque S, Lavergne C, Cara H, de Lepine P, Tarriere C, Gueguen B (1994) An on-board system for detecting lapses of alertness in car driving. In: Proceedings of the 14th E.S.V. conference session 2 – intelligent vehicle highway system and human factors, Munich

    Google Scholar 

  • Barr L, Popkin S, Howarth (2009) An evaluation of emergin driver fatigue detection measures and technologies. Federal Motor Carrier Safety Administration, Report No: FMCSA-RRR-09-005

    Google Scholar 

  • Bergasa L, Barea R, Lopez E, Escudero M, Boquete L, Pinedo J (2003) Facial features tracking applied to driver drowsiness detection. In: Proceedings of the 21st IASTED international conference on applied informatics, Austria, Feb 2003, pp 378–825

    Google Scholar 

  • Brown ID (1966) Effects of prolonged driving upon driving skills and performance of a subsidiary task. Ind Med Surg 35:760–765

    Google Scholar 

  • Campagne A, Pebayle T, Muzet A (2004) Correlation between driving errors and vigilance level: influence of the driver age. Physiol Behav 80:515–524

    Article  Google Scholar 

  • Carskadon M, Dement W (1981) Cumulative effects of sleep restrictions on daytime sleepiness. Psychology 18:107–118

    Google Scholar 

  • Dinges DF (1995) An overview of sleepiness and accidents. J Sleep Res 4(Suppl 2):4–14

    Article  Google Scholar 

  • Dingus TA, Hardee L, Wierwille WW (1985) Development of Impaired Driver Detection Measures. Department of Industrial Engineering and Operations Research, Virginia Polytechnic Institute and State University, (Departmental Report 8504), Blacksburg

    Google Scholar 

  • Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York

    MATH  Google Scholar 

  • Elling M, Sherman P (1994) Evaluation of steering wheel measures for drowsy drivers. In: 27th ISATA, Aachen, Germany, pp 207–214

    Google Scholar 

  • Eskandarian A, Mortazavi A (2007) Evaluation of a Smart algorithm for commercial vehicle driver drowsiness detection. In: IEEE Intelligent Vehicle Symposium, Istanbul, Turkey

    Google Scholar 

  • Eskandarian A, Mortazavi A (2008) An unobtrusive driver drowsy technology, provisional patent, Serial No. 61/193191, applied 5 Nov 2008, currently patent pending

    Google Scholar 

  • Eskandarian A, Mortazavi A (2009) Unobtrusive driver drowsiness detection system and method, US Patent Application No.12/613,306, filed 5 Nov 2009

    Google Scholar 

  • FARS (2008) National Highway Traffic Safety Administration, National Center for Statistics and Analysis, FARS Analytic Reference Guide, 1975 to 2008. US Department of Transportation, Washington, DC

    Google Scholar 

  • Fell D (1994) Safety update: problem definition and countermeasure summary: fatigue. New South Wales Road Safety Bureau, RUS No. 5

    Google Scholar 

  • Flandrin P, Goncalves P (2004) Empirical mode decompositions as data-driven wavelet-like expansions. Int J Wavelets, Multi-Resol Inform Process 2(4):477–496

    Article  MathSciNet  MATH  Google Scholar 

  • Fukuda J, Akutsu E, Aoki K (1995) Estimation of driver’s drowsiness level using interval of steering adjustment for lane keeping. JSAE Rev, Soc Automot Eng Japan 16(2):197–199

    Google Scholar 

  • Gander P, James I (1999) Investigating fatigue in truck crashes. Report in Wellington school of medicine and Commercial Vehicle Investigation in New Zealand

    Google Scholar 

  • Harris W et al (1972) A study of the relationships among fatigue, hours of service, and safety of operationsof truck and bus drivers, BMCS-RD-71-2. US Department of Transportation, Washington, DC

    Google Scholar 

  • Hertz RP (1988) Tractor-trailer driver fatality: the role of non-connective rest in a sleeper berth. Accid Anal Prev 20(6):429–431

    Article  Google Scholar 

  • Horne J, Reyner L (1995) Sleep related vehicle accidents. Brit Med J 310(6979):565–567

    Article  Google Scholar 

  • Huang NE, Shen SS (2005) The Hilbert-Huang transform and Its applications (Interdisciplinary mathematical sciences). World Scientific Publishing Company, Singapore, pp 324

    Google Scholar 

  • Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1996) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society A, Mathematical, Physical and Engineering Sciences, vol 454, London, UK, pp 903–995

    Google Scholar 

  • Hulbert S (1972) Effects of driver fatigue. In: Forbes TW (ed) Human factors in highway traffic safety research. Wiley, New York

    Google Scholar 

  • Jovanis P, Kaneko T, Lin T (1991) Exploratory analysis of motor carrier accident risk and daily driving patterns. 70th annual meeting of Transportation Research Board, Transportation, Washington, DC

    Google Scholar 

  • Lal S, Craig A (2001) A critical review of psychophysiology of driver fatigue. Biol Psychol 55:173–194

    Article  Google Scholar 

  • Mackie RR, O’Hanlon JF (1977) A study of the combined effects of extended driving and heat stress on driver arousal and performance. In: Mankie RR (ed) Vigilance: theory, operational performance and physiological correlates. Plenum Press, New York

    Google Scholar 

  • Mackie R, Wylie CD (1991) Countermeasures to loss of alertness in motor vehicle drivers: a taxonomy and evolution. In: Proceedings of the human factors society 35th annual meeting, San Francisco, CA, pp 1149–1153

    Google Scholar 

  • Mast T, Jones H, Heimstra N (1966) Effects of fatigue on performance in a driving device. Highw Res Rec. Cited in Haworth, Vulcan, Triggs, and Fildes, Driver fatigue research: development of methodology. Accident Research Center, Monash University Australia, 1989

    Google Scholar 

  • McCartt T, Ribner S, Pack A, Hammer M (1996) The scope and nature of the drowsy driving problem in the New York State. Accid Anal Prev 28:511–517

    Article  Google Scholar 

  • McCartt A, Rohrbaugh J, Hammer M, Fuller S (2000) Factors associated with falling asleep at the wheel among long-distance truck drivers. Accident Anal Prev 32:493–504

    Article  Google Scholar 

  • Mitler M, Miller J, Lipsitz J, Walsh K, Wylie C (1997) The sleep of long-haul truck drivers. N Engl J Med 337(11):755–761

    Article  Google Scholar 

  • Mortazavi (2005) http://www.aaafoundation.org/multimedia/press_releases/PoliceDDFS.cfm

  • Mortazavi A, Eskandarian A, Sayed RA (2009) Effect of drowsiness on driving performance variables of commercial vehicle drivers. Int J Automot Engin 10(3):391–404

    Article  Google Scholar 

  • NASS/GES (2008) National Highway Traffic Safety Administration), National Center for Statistics and Analysis. National Automotive Sampling System (NASS): general estimates system, analytical user’s manual 1988–2008. US Department of Transportation, Washington, DC

    Google Scholar 

  • Ogawa K, Shimotani M (1997) Drowsiness detection system. Mitsubishi Electric Adv 78:13–16

    Google Scholar 

  • Pilutti T, Ulsoy G (1997) Identification of driver state for lane-keeping tasks: experimental results. In: The American Control Conference, Seattle, pp 1667–1671

    Google Scholar 

  • Planque S, Petit C, Chapeau D (1991) A system for identifying lapses of alertness when driving. Renault, France

    Google Scholar 

  • Riemersama JB, Sanders AF, Wildervack C, Gaillard AW (1977) Performance decrement during prolonged night driving. In: Makie RR (ed) Vigilance: theory, operational performance and physiological correlates. Plenum Press, New York

    Google Scholar 

  • Rilling G, Flandrin P and Gonçalvès P (2003) On empirical mode decomposition and its algorithms. In: IEEE-EURASIP workshop on nonlinear signal and image processing, grado (I), Baltimore

    Google Scholar 

  • Ryder J, Malin S and Kinsley C (1981) The effects of fatigue and alcohol on highway safety. NHTSA Report No. DOT-HS-805-854, Washington, DC

    Google Scholar 

  • Safford R, Rockwell TH (1967) Performance decrement in twenty four hour driving. Highw Res Rec

    Google Scholar 

  • Sagberg F (1999) Road accidents caused by drivers falling asleep. Accident Anal Prev 31:639–649

    Article  Google Scholar 

  • Samel A et al (1995) Jet lag and sleepiness in aircrew. J Sleep Res 4(2):30–36

    Article  Google Scholar 

  • Sayed R, Eskandarian A (2001) Unobtrusive drowsiness detection by neural network learning of driver steering. J Autom Engin Proc Instit Mech Engin, Part D 215:969–975

    Article  Google Scholar 

  • Sayed (2011) http://www.nhtsa.gov/FARS

  • Seki M, Shimotani M, Nishida M (1998) Study of blink detection using bright pupils. JSAE Review, Society of Automotive Engineers of Japan 19(1):58–60

    Google Scholar 

  • Seko Y (1984) Present technological status of detecting drowsy driving patterns. Jidosha Gijutsu 30(5), Central research Institute, Nissan Motor Company, Tokyo

    Google Scholar 

  • Siegmund GP, King DJ, Mumford DK (1996) Correlation of steering behavior with heavy-truck driver fatigue. SAE Special Publications, vol 1190, pp 17–38

    Google Scholar 

  • Skipper JH, Wierwille W and Hardee L (1984) An investigation of low level stimulus induced measures of driver drowsiness. Virginia Polytechnic Institute and State University IEOR Department Report #8402, Blacksburg, Virginia

    Google Scholar 

  • Stein AC (1995) Detecting fatigued drivers with vehicle simulators. In: Hartley L (ed) Driver impairment, driver fatigue and driving simulation. Taylor & Francis, Bristol, pp 133–150

    Google Scholar 

  • Stoohs R, Guilleminault C, Dement W (1993) Sleep apnea and hypertension in commercial truck drivers. Sleep 16:S11–S14

    Google Scholar 

  • Strohl KP et al (1998) NCSDR/NHTSA expert panel on driver fatigue and Sleepiness. drowsy driving and automobile crashes. Report and recommendations. National Highway Traffic Safety Administration, Washington, DC (DOT HS 808 707)

    Google Scholar 

  • Stutts J, Wilkins J, Osberg S, Vaughn B (2003) Driver risk factors for sleep-related crashes. Accident Anal Prev 35:321–331

    Article  Google Scholar 

  • Sweeney M, Ellingstad V, Mayer D, Eastwood M, Weinstein E, Loeb B (1995) The need for sleep: discriminating between fatigue-related and non-fatigue related truck accidents. In: Human factors and ergonomics society meeting, vol 2, Baltimore, pp 1122–1126

    Google Scholar 

  • Thiffault P, Bergeron J (2003a) Monotony of road environment and driver fatigue: a simulator study. Accident Anal Prev 35:381–391

    Article  Google Scholar 

  • Thiffault P, Bergeron J (2003b) Fatigue and individual differences in monotonous simulated driving. Personal Indiv Diff 34:159–176

    Article  Google Scholar 

  • Tilley DH, Erwin CW, Gianturco DT (1973) Drowsiness and driving: preliminary report of a population survey. SAE, international automotive engineering congress, Detroit Michigan, 8–12 Jan 1973

    Google Scholar 

  • Torsvall L, Åkerstedt T (1988) Extreme sleepiness: quantification of EOG and spectral EEG parameters. Int J Neurosci 38(3–4):435–441

    Article  Google Scholar 

  • Wierwille W, Ellsworth L (1994) Evaluation of driver drowsiness by trained raters. Accident Anal Prev 26(5):571–581

    Article  Google Scholar 

  • Wierwille WW, Wreggit SS, Mitchell MW (1992) Research on vehicle based driver status/performance monitoring. First Semi-Annual Research Report. NHTSA Cooperative Agreement Number DTNH 22-91-Y-07266, Washington, DC

    Google Scholar 

  • Wu R, Lin C, Liang S, Huang T, Chen Y, Jung T (2004) Estimating driving performance based on EEG spectrum and fuzzy neural network. IEEE Int Conf Neural Netw 1:585–590

    Google Scholar 

  • Yabuta K, Iizuka H, Yanagishima T, Kataoka Y, Seno T (1985) The development of drowsiness warning devices. In: Proceedings of the 10th international technical conference on experimental safety vehicles, Washington

    Google Scholar 

  • Young T, Blustein J, Finn L, Palta M (1997) Sleep-disordered breathing and motor vehicle accidents in a population-based sample of employed adults. Sleep 20(8):608–613

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for Intelligent Systems Research, The George Washington University, Washington, DC, 20052, USA

    Prof. Azim Eskandarian D.Sc. (Professor of Engineering and Applied Science and Director of Center for Intelligent Systems Research)

  2. Partners for Advanced Transportation Technology (PATH), University of California, 2105 Bancroft Way, 94720-3830, Berkeley, CA, USA

    Dr. Ali Mortazavi (Senior Development Engineer)

  3. Mechanical Department, NWFP University of Engineering and Technology, PO Box 814, 25000, Peshawar, North West Frontier Provi, Pakistan

    Riaz Akbar Sayed Dr. (Professor of Engineering and director of Institute of Mechatronics Engineering)

Authors
  1. Prof. Azim Eskandarian D.Sc.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dr. Ali Mortazavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Riaz Akbar Sayed Dr.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Azim Eskandarian D.Sc. .

Editor information

Editors and Affiliations

  1. Center for Intelligent Systems Research, The George Washington University, 801 22nd Street, NW, Phillips Hall 630, Washington, DC, 20052, USA

    Azim Eskandarian

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag London Ltd.

About this entry

Cite this entry

Eskandarian, A., Mortazavi, A., Sayed, R.A. (2012). Drowsy and Fatigued Driving Problem Significance and Detection Based on Driver Control Functions. In: Eskandarian, A. (eds) Handbook of Intelligent Vehicles. Springer, London. https://doi.org/10.1007/978-0-85729-085-4_36

Download citation

Publish with us

Policies and ethics

Search

Navigation