Drowsy and Fatigued Driver Warning, Counter Measures, and Assistance

  • Riaz Akbar Sayed
  • Azim Eskandarian
  • Ali Mortazavi


Driving under the influence of fatigue and sleepiness is a serious safety concern. Hundreds of lives and billions of dollars are lost every year due to accidents caused by driver drowsiness. There are many aspects to the problem of a driver falling asleep while driving that include causes, detection, monitoring, warning, and countermeasures against drowsy driving. A number of crucial design issues have to be considered before the anticipated benefits of the drowsy driving warning can be fully realized. In this chapter the two major aspects, that is, warning and countermeasures, are discussed.

Warning means to convey to the driver about his/her state of sleepiness/drowsiness so that corrective actions can be taken. There are many issues related to warning system design but the two main concerns are when and how to warn the driver, that is, alarm modality and alarm timing. Although there are no standard guidelines for selection and design of appropriate alarm modalities, at least three types of modalities (visual, audio, and haptic/tactile) and their combinations are possible for any alarm design. An important component of collision avoidance system is the algorithm that determines the timing of warning. A poorly timed alarm may actually undermine the safety of the driver. An alert issued too early may be ignored by drivers if they are unable to perceive the cause of the warning. On the other hand, if it occurs too late, it may be viewed as ineffective.

An alarm that does not represent the true state of driver drowsiness, that is, the driver is not drowsy but the system issues a warning, is called false alarm. False and nuisance alarms are a particular problem for automotive collision avoidance and warning systems.

A comprehensive review of the literature regarding driver fatigue/drowsiness warning research, the present state of research and technologies being developed, and issues related to warning/alarm design and the future trends are highlighted. Driver fatigue-related countermeasures are also discussed along with their merits and demerits.


False Alarm False Alarm Rate Warning System Auditory Display Collision Avoidance System 


  1. Aldrich MS (1989) Automobile accidents in patients with sleep disorders. Sleep 12(6):487–494MathSciNetGoogle Scholar
  2. Abe G, Itoh M, Tanaka K (2002) Dynamics of driver’s trust in warning systems. In: Proceedings of the IFAC world congress, BarcelonaGoogle Scholar
  3. Abe G, Richardson J (2004) The effect of alarm timing on driver behavior: an investigation of differences in driver trust and response to alarms according to alarm timing. Transport Res Part F 7:307–322CrossRefGoogle Scholar
  4. Anund A, Kecklunda G, Vadebyb A, Hjälmdahl M, Åkerstedt T (2008) The alerting effect of hitting a rumble strip—a simulator study with sleepy drivers. Accid Anal Prev 40(2008):1970–1976CrossRefGoogle Scholar
  5. Carskadon MA, Dement WC (1981) Cumulative effects of sleep restriction on daytime sleepiness. Psychophysiology 18:107–118CrossRefGoogle Scholar
  6. Dingus TA, Jahns SK, Horowitz AD, Knipling R (1998) Human factors design issues for crash avoidance systems. In: Barfield W, Dingus TA (eds) Human factors in intelligent transportation systems. pp 55–93Google Scholar
  7. Edman TR (1982) Human factors guide lines for the use of synthetic speech devices. In: Proceedings of the human factor society 26th annual meeting, Human Factors Society, Santa Monica, 1982. pp 212–216Google Scholar
  8. Edworthy J (1994) The design and implementation of nonverbal auditory warnings. Appl Ergon 25(4):202–210CrossRefGoogle Scholar
  9. Farber E, Paley M (1993) Using freeway traffic data to estimate the effectiveness of rear end collision countermeasures. In: 3rd annual IVHS America meeting, IVHS America Washington DCGoogle Scholar
  10. Findley LJ, Unverzagt ME, Suratt PM (1988) Automobile accidents involving patients with obstructive sleep apnea. Am Rev Respir Dis 138:337–340Google Scholar
  11. Gander P, Marshall N, Bolger W, Girling I (2005) An evaluation of driver training as a fatigue countermeasure. Transp Res Part F Traffic Psychol Behav 8:47–58CrossRefGoogle Scholar
  12. Gershon P, Shinar D, Ronen A (2009) Evaluation of experience based fatigue countermeasures. Accid Anal Prev 41(2009):969–975CrossRefGoogle Scholar
  13. Gold DR et al (1992) Rotating shift work, sleep, and accidents related to sleepiness in hospital nurses. Am J Public Health 82(7):1011–1014CrossRefGoogle Scholar
  14. Hertz RP (1988) Tractor-trailer driver fatality: the role of non-connective rest in a sleeper berth. Accid Anal Prev 20(6):429–431CrossRefGoogle Scholar
  15. Horowitz AD, Dingus TA (1992) Warning signal design: a key to human factors issue in an in-vehicle front to rear end collision warning system. In: Proceedings of the human factors society, vol 36, pp 1011–1013Google Scholar
  16. Janssen W, Nilson L (1993) Behavioral effects of driver support. In: Parkes AM, Fransen S (eds) Driving future vehicle. Taylor & Francis, London, pp 147–155Google Scholar
  17. Jovanis PP, Kaneko T, Lin TD (1991) Exploratory analysis of motor carrier accident risk and daily driving patterns. In: 70th annual meeting of Transportation Research Board, Transportation Research Board, Washington, DC, 1991Google Scholar
  18. Kantowitz BH, Hanowski RJ, Kantowitz SC (1997) Driver acceptance of unreliable trac information in familiar and unfamiliar settings. Hum Factors 39:164–176CrossRefGoogle Scholar
  19. Knipling RR, Mironer M, Hendricks DL, Tijerna L, Everson JC, Wilson C (1993) Assessment of IVHS countermeasures for collision avoidance: rear-end crashes, NTIS No. DOT-HS-807-995. National Highway Traffic Safety Administration, Washington, DCGoogle Scholar
  20. Landstrom U, Englund K, Nordstrom B, Astrom A (1999) Sound exposure as a measure against driver drowsiness. Ergonomics 42(7):927–937CrossRefGoogle Scholar
  21. Lauber JK, Kayten PJ (1998) Sleepiness, circadian dysrhythmia, and fatigue in transportation system accidents. Sleep 11:503–512Google Scholar
  22. Lee JD, Moray N (1992) Trust, control strategies and allocation of function in human-machine systems. Ergonomics 35:1243–1270CrossRefGoogle Scholar
  23. Lee JD, Hoffman JD, Hayes E (2004) Collision warning design to mitigate driver distraction, CHI 2004, Vienna, 24–29 Apr 2004Google Scholar
  24. Lesch MF (2003) Comprehension and memory for warning symbols: age-related differences and impact of training. J Saf Res 34:495–505CrossRefGoogle Scholar
  25. Lloyd MM, Wilson GD, Nowak CJ, Bittner AC (1999) Brake pulsing as haptic warning for an intersection collision avoidance countermeasure. Transp Res Rec 1694:34–41CrossRefGoogle Scholar
  26. Mackie RR, Miller JC (1978) Effects of hours of service, regularity of schedules, and cargo loading on truck and bus driver fatigue, DOT-HS-5-01142. Human Factors, GoletaGoogle Scholar
  27. Marcus CL, Loughlin GM (1996) Effect of sleep deprivation on driving safety in housestaff. Sleep 19(10):763–766Google Scholar
  28. McCartt AT, Ribner SA, Pack AI, Hammer MC (1996) The scope and nature of the drowsy driving problem in New York state. Accid Anal Prev 28(6):511–517CrossRefGoogle Scholar
  29. McCartt AT, Rohrbaugh JW, Hammer MC, Fuller SZ (2000) Factors associated with falling asleep at the wheel among long-distance truck drivers. Accid Anal Prev 32(4):493–504CrossRefGoogle Scholar
  30. Miles JD, Pratt MP, Carlson PJ (2006) Evaluation of erratic maneuvers associated with installation of rumble Strips. J Transp Res Board 1973:73–79CrossRefGoogle Scholar
  31. Mitler MM, Carskadon MA, Czeisler CS, Dement WC, Dinges DF, Graeber RC (1988) Catastrophes, sleep, and public policy. Sleep 11(1):100–109Google Scholar
  32. Mitler MM, Miller JC, Lipsitz JJ, Walsh JK, Wylie CD (1997) The sleep of long-haul truck drivers. N Engl J Med 337(11):755–761CrossRefGoogle Scholar
  33. Muir BM, Moray N (1996) Trust in automation: part II. Experimental studies of trust and human intervention in a process control simulation. Ergonomics 39:429–460CrossRefGoogle Scholar
  34. Noyce DA, Elango VV (2004) Safety evaluation of centerline rumble strips. J Transp Res Board 1862:44–53CrossRefGoogle Scholar
  35. Parasuraman R, Molloy R, Singh IL (1993) Performance consequences of automation- induced “complacency.” Int J Aviation Psychol 3:1–23CrossRefGoogle Scholar
  36. Parasuraman R, Riley V (1997) Humans and automation: use, misuse, disuse, abuse. Hum Factors 39(2):230–253CrossRefGoogle Scholar
  37. Parasuraman R, Hancock PA, Olofinboba O (1997) Alarm effectiveness in driver centered collision warning systems. Ergonomics 40(3):390–399CrossRefGoogle Scholar
  38. Presaud BN, Retting RA, Lyon CL (2003) Crash reduction following installation of centerline rumble strips on rural two-lane roads. Ryerson University, TorontoGoogle Scholar
  39. Pritchett AR (2001) Reviewing the role of cockpit alerting systems. Hum Factors Aerosp Soc 1:5–38Google Scholar
  40. Radun I, Radun JE (2009) Convicted of fatigued driving: who, why and how? Accid Anal Prev 41(2009):869–875CrossRefGoogle Scholar
  41. Rempel JK, Holmes JG, Zanna MP (1985) Trust in close relationships. J Pers Soc Psychol 49:95–112CrossRefGoogle Scholar
  42. Royal D (2003) National survey of distracted and drowsy driving attitudes and behavior: 2002 (vol 1: Findings), DOT HS 809 566. US Department of Transportation NHTSA, Washington, DCGoogle Scholar
  43. Satchell P (1993) Cockpit monitoring and alerting systems. Ashgate, AldershotGoogle Scholar
  44. Sheridan TB (1988) Trustworthiness of command and control systems. In: Proceedings of the IFAC/IFIP/IFORS/IEA conference on analysis, design, and evaluation of man-machine systems, Oulu, Finland. pp 427–431Google Scholar
  45. Stokes A, Wickens C, Kite K (1990) Display technology: human factors concepts. Society of Automotive Engineers, Washington DCGoogle Scholar
  46. Stoohs RA, Guilleminault C, Dement WC (1993) Sleep apnea and hypertension in commercial truck drivers. Sleep 16:S11–S14Google Scholar
  47. Strohl KP, Blatt J, Council F (1998) Drowsy driving and automobile crashes. Report of NCSDR/NHTSA expert panel on driver fatigue and sleepiness, DOT HS 808 707. US Department of Transportation NHTSA, Washington, DCGoogle Scholar
  48. Suzuki K, Jansson H (2003) An analysis of driver steering behavior during auditory or haptic warnings for the design of lane departure warning system. JSAE Rev 24(2003):65–70CrossRefGoogle Scholar
  49. Swets JA, Pickett RM (1982) Evaluation of diagnostic systems. Academic, New YorkGoogle Scholar
  50. Webber JW, Mullins CA, Schumacher PW, Wright CD (1994) A system approach to the development of an integrated collision avoidance vehicle. In: Proceedings of the vehicle navigation and information systems conference, pp 431–434Google Scholar
  51. Wheeler WA, Campbell JL, Kinghorn RA (1998) Commercial vehicle-specific aspects of intelligent transportation systems. In: Barfield W, Dingus TA (eds) Human factors in intelligent transportation systems. Erlbaum, Mahwah, pp 95–130Google Scholar
  52. Wiener EL (1988) Cockpit automation. In: Wiener EL, Nagel DC (eds) Human factors in aviation. Academic, San Diego, pp 433–461Google Scholar
  53. Wilkinson T, Edwards S, Haines E (1966) Performance following a night of reduced sleep. Psychon Sci 5:471–472Google Scholar
  54. Wylie CD, Schultz T, Miller JC, Mitler MM, Mackie RR (1996) Commercial motor vehicle driver fatigue and alertness study: technical summary, MC-97-001. Federal Highway Administration, Washington, DCGoogle Scholar
  55. 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):608–613Google Scholar

Copyright information

© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  • Riaz Akbar Sayed
    • 1
  • Azim Eskandarian
    • 2
  • Ali Mortazavi
    • 3
  1. 1.Mechanical DepartmentNWFP University of Engineering and TechnologyPeshawarPakistan
  2. 2.Center for Intelligent Systems ResearchThe George Washington UniversityWashingtonUSA
  3. 3.Partners for Advanced Transportation Technology (PATH)University of CaliforniaBerkeleyUSA

Personalised recommendations