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Encyclopedia of Sustainability Science and Technology pp 3108–3135Cite as

Driver Inattention Monitoring System for Intelligent Vehicles

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Definition of the Subject

Driver inattention is a major factor in highway crashes. The National Highway Traffic Safety Administration (NHTSA) estimates that approximately 25% of police-reported crashes involve some forms of driver inattention – the driver is distracted, asleep or fatigued, or otherwise “lost in thought” [1]. This entry reviews the state-of-the-art technologies for monitoring driver inattention, which can be classified into two main categories: distraction and fatigue. Driver inattention is a major factor in most traffic accidents. Research and development has been actively carried out for decades with the goal of precisely determining the drivers’ state of mind. This entry summarizes these approaches by dividing them into five different types of measures:

  1. 1.

    Subjective report measures

  2. 2.

    Driver biological measures

  3. 3.

    Driver physical measures

  4. 4.

    Driving performance measures

  5. 5.

    Hybrid measures

Among these approaches, subjective report measures and driver biological...

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Abbreviations

DIMS:

Driver inattention monitoring system, to monitor the attention status of the driver.

Distraction:

Driver distraction is a diversion of attention away from activities critical for safe driving toward a competing activity.

Driver biological:

Utilize driver biological signals, e.g., electroencephalography (EEG), electrocardiogram (ECG), electro-oculography (EOG), surface electromyogram (sEMG), to estimate driver attention status.

Driver physical:

Utilize driver’s physical signals, e.g., eye closure duration, blink frequency, nodding frequency, fixed gaze, and frontal face pose, to estimate driver attention status.

Driving performance:

Utilize driving performance, e.g., pressure distribution on the seat, car-following, steering wheel angle, accelerator pedal position, lane boundaries, and upcoming road curvature, to estimate driver attention status.

Fatigue:

Driver fatigue refers to a combination of symptoms such as impaired performance and a subjective feeling of drowsiness.

Hybrid:

Combining driver physical measures with driving performance measures to estimate driver attention status.

Inattention:

Driver inattention represents diminished attention to activities that are critical for safe driving in the absence of a competing activity.

Measures:

The way to estimate the driver’s attention status.

Physical signal extraction:

The approaches for extracting driver physical signals.

Subjective report:

Subjective self-assessment of attention status.

Bibliography

  1. Ranney TA, Mazzae E, Garrott R, Goodman MJ (2000) Nhtsa driver distraction research: past, present, and future. Technical report, Transportation Research Center Inc

    Google Scholar 

  2. Lee JD, Young KL, Regon MA (2009) Driver distraction:. CRC Press/Taylor & Francis Group, London

    Google Scholar 

  3. Stutts JC, Reinfurt DW, Staplin L, Rodgman EA (2001) The role of driver distraction in traffic crashes. Technical report, American Automobile Association AAA Foundation for Traffic Safety

    Google Scholar 

  4. S.S.G. on Fatigue (2006) Canadian operational definition of driver fatigue. Technical report, STRID Sub-Group on Fatigue

    Google Scholar 

  5. Brill JC, Hancock PA, Gilson RD (2003) Driver fatigue: is something missing? In: Proceedings of the second international driving symposium on human factors in driver assessment, training and vehicle design, Park City, pp 138–142

    Google Scholar 

  6. T.R.S. for the Prevention of Accidents (2001) Driver fatigue and road accidents: a literature review and position paper. Technical report, The Royal Society for the Prevention of Accidents

    Google Scholar 

  7. Croo HD, Bandmann M, Mackay GM, Rumar K, Vollenhoven P (2001) The role of driver fatigue in commercial road transport crashes. Technical report, European Transport Safety Council

    Google Scholar 

  8. Horne J, Reyner L (1999) Vehicle accidents related to sleep: a review. Occup Environ Med 56(5):289–294

    Article  CAS  Google Scholar 

  9. Nilson T, Nelson TM, Carlson D (1997) Accid Anal Prev 29(4):479

    Article  Google Scholar 

  10. Endsley MR (1995) Hum Factors 37(1):32

    Article  Google Scholar 

  11. http://www.chalmers.se/safer/driverdistraction-en/programme

  12. Angell L, Auflick J, Austria A, Kochhar D, Tijerina L, Biever W, Diptiman T, Hogsett J, Kiger S (2006) Driver workload metrics project-task 2 final report. Technical report, U.S. Dept. of Transportation National Highway Traffic Safety Administration

    Google Scholar 

  13. Harbluk JL, Noy YI, Trbovich PL, Eizenman M (2007) Accid Anal Prev 39:372

    Article  Google Scholar 

  14. Rantanen EM, Goldberg JH (1999) The effect of mental workload on the visual field size and shape. Ergonom 42(6):816–834

    Article  CAS  Google Scholar 

  15. Harbluk JL, Noy YI (2002) The impact of cognitive distraction on driver visual behaviour and vehicle control. Technical report, Ergonomics Division, Road Safety Directorate and Motor Vehicle Regulation Directorate

    Google Scholar 

  16. Hayhoe MM (2004) Infancy 6(2):267

    Article  Google Scholar 

  17. May JG, Kennedy RS, Williams MC, Dunlap WP, Brannan JR (1990) Acta Psychol 75(1):75

    Article  CAS  Google Scholar 

  18. Miyaji M, Kawanaka H, Oguri K (2009) Driver’s cognitive distraction detection using physiological features by the adaboost. In: Proceedings of the 12th international IEEE conference on intelligent transportation systems, St. Louis, 2009

    Google Scholar 

  19. Liang Y, Lee JD (2010) Accid Anal Prev 42:881

    Article  Google Scholar 

  20. Zhang H, Smith M, Dufour R (2008) A final report of safety vehicles using adaptive interface technology (phase ii: Task 7c): visual distraction. Technical report, Delphi Electronics and Safety

    Google Scholar 

  21. Itoh M (2009) Individual differences in effects of secondary cognitive activity during driving on temperature at the nose tip. In: Proceedings of the 2009 IEEE International conference on mechatronics and automation, Changchun, China, 2009

    Google Scholar 

  22. Wesley A, Shastri D, Pavlidis I (2010) A novel method to monitor driver’s distractions. In: Proceedings of the 28th of the international conference extended abstracts on human factors in computing systems CHI EA 10 (2010), Atlanta, 2010

    Google Scholar 

  23. Berka C, Levendowski DJ, Lumicao MN, Yau A, Davis G, Zivkovic VT, Olmstead RE, Tremoulet PD, Craven PL (2007) Aviat Space Environ Med 78(5):B231

    Google Scholar 

  24. Ranney TA (2008) Driver distraction: a review of the current state-of-knowledge. Technical report, NHTSA

    Google Scholar 

  25. Zhou H, Itoh M, Inagaki T (2008) Influence of cognitively distracting activity on driver’s eye movement during preparation of changing lanes. In: SICE annual conference, Tokyo, 2008

    Google Scholar 

  26. Carsten OMJ, Brookhuis K (2005) Transp Res Part F: Traffic Psychol Behav 8:191

    Article  Google Scholar 

  27. Eskandarian A, Sayed R, Delaigue P, Blum J, Mortazavi A (2007) Advanced driver fatigue research. Technical report, Report No. FMCSA-RRR-07-001. U.S. Department of Transportation Federal Motor Carrier Safety Administration

    Google Scholar 

  28. Dinges DF, Richard G (1998) Perclos: a valid psychophysiological measure of alertness as assessed by psychomotor vigilance. Technical report, Federal Highway Administration, Office of Motor Carriers

    Google Scholar 

  29. Svensson U, (2004) Electrooculogram analysis and development of a system for defining stages of drowsiness. Master’s thesis, Department of Biomedical Engineering, Linkoping University, Sweden

    Google Scholar 

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

    Google Scholar 

  31. Mast T, Jones H, Heimstra N (1989) Effects of fatigue on performance in a driving device. Highway research record. Technical report, Driver Fatigue Research: Development of Methodology, Haworth, Vulcan, Triggs, and Fildes (eds) Accident Research Center, Monash University Australia

    Google Scholar 

  32. Kahneman D (1973) Attention and effort. Technical report, Prentice Hall, Englewood Cliffs

    Google Scholar 

  33. 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, DC

    Google Scholar 

  34. Dingus TA, Hardee L, Wierwille WW (1985) Development of impaired driver detection measures. Technical report, Department of Industrial Engineering and Operations Research, Virginia Polytechnic Institute and State University, (Departmental Report 8504), Blacksburg

    Google Scholar 

  35. Elling M, Sherman P (1994) Evaluation of steering wheel measures for drowsy drivers. In 27th ISATA, Aachen, Germany

    Google Scholar 

  36. Zhong YJ, Du LP, Zhang K, Sun XH (2007) Wavelet Anal Pattern Recognit 4:1843

    Google Scholar 

  37. Skipper JH, Wierwille W, Hardee L (1984) An investigation of low level stimulus induced measures of driver drowsiness. Technical report, Virginia Polytechnic Institute and State University IEOR Department Report No.8402, Blacksburg

    Google Scholar 

  38. Stein AC (1995) Detecting fatigued drivers with vehicle simulators. Technical report, Drive r Impairment, Driver Fatigue and Driving Simulation, Hartley L (ed) Taylor & Francis, Bristol, pp 133–150

    Google Scholar 

  39. Safford R, Rockwell TH (1967) Highway Res Record 163:68

    Google Scholar 

  40. Riemersama JB, Sanders AF, Wildervack C, Gaillard AW (1977) Performance decrement during prolonged night driving. Technical report, Vigilance: Theory, Operational Performance and Physiological Correlates, Makie RR (ed) Plenum Press, New York

    Google Scholar 

  41. www.saabnet.com/tsn/press/071102.html

  42. Nabo A (2009) Driver attention – dealing with drowsiness and distraction. Technical report, Saab Automobile AB

    Google Scholar 

  43. www.smarteye.se

  44. www.testdriven.co.uk/lexus-ls-600h/

  45. www.emercedesbenz.com

  46. www.media.volvocars.com

  47. Barr L, Popkin S, Howarth H (2009) An evaluation of emerging driver fatigue detection measures and technologies. Technical report, Volpe National Transportation Systems Center

    Google Scholar 

  48. www.seeingmachines.com

  49. www.smivision.com

  50. Bergasa L, Nuevo J, Sotelo M, Barea R, Lopez E (2006) IEEE Trans Intell Transp Syst 7(1):63

    Article  Google Scholar 

  51. Liang Y, Reyes ML, Lee JD (2007) IEEE Trans Intell Transp Syst 8(2):340

    Article  Google Scholar 

  52. Freund Y, Schapire RE (1997) J Comput Syst Sci 55:119

    Article  Google Scholar 

  53. Lee J, Reyes M, Liang Y, Lee YC (2007) Safety vehicles using adaptive interface technology (task 5): algorithms to assess cognitive distraction. Technical report, The University of Iowa

    Google Scholar 

  54. Sathyanarayana A, Boyraz P, Hansen JHL (2008) Driver behavior analysis and route recognition by hidden markov models. In: Proceedings of the 2008 IEEE international conference on vehicular electronics and safety, Columbus, 2008

    Google Scholar 

  55. Kaida K, Takahashi M, Akerstedt T, Nakata A, Otsuka Y, Haratani T, Fukasawa K (2006) Clin Neurophysiol 117(7):1574

    Article  Google Scholar 

  56. Ingre M, Akerstedt T, Peters B, Anund A, Kecklund G (2006) J Sleep Res 15(1):47

    Article  Google Scholar 

  57. Craig A, Tran Y, Wijesuriya N, Boord P (2006) Biol Psychol 72:78

    Article  Google Scholar 

  58. Jap BT, Lal S, Fischer P, Bekiaris E (2009) Expert SystAppl 36(2):2352

    Article  Google Scholar 

  59. Chouvarda I, Papadelis C, Papadeli CK, Bamidis PD, Koufogiannis D, Bekiaris E, Maglaveras N (2007) Medinfo 12(2):1294

    Google Scholar 

  60. Yeo MVM, Li XP, Shen K, Smith EPW (2009) Safety Sci 47(1):115

    Article  Google Scholar 

  61. Shen KQ, Li XP, Ong CJ, Shao SY, Smith EPW (2008) Clin Neurophysiol 119:1524

    Article  Google Scholar 

  62. Lin CT, Wu RC, Liang SF, Chao WH, Chen YJ, Jung TP (2005) Circuits and systems I: regular papers, IEEE Trans 52(12):2726

    Google Scholar 

  63. Lin CT, Chen YC, Huang TY, Chiu TT, Ko LW, Liang SF, Hsieh HY, Hsu SH, Duann JR (2008) Biomed Eng IEEE Trans 55((5):1582

    Article  Google Scholar 

  64. Liu J, Zhang C, Zheng C (2010) Biomed Signal Process Control 5:124

    Article  Google Scholar 

  65. Skinner BT, Nguyen HT, Liu DK (2007) Classification of eeg signals using a genetic-based machine learning classifier. In: 29th annual international conference of the IEEE, Lyon. Engineering in Medicine and Biology Society, pp 3120–3123

    Google Scholar 

  66. Yang G, Lin Y, Bhattacharya P (2010) Inf Sci 180:1942

    Article  Google Scholar 

  67. Chua CP, McDarby G, Heneghan C (2008) Physiol Meas 29(8):857

    Article  Google Scholar 

  68. Damousis IG, Tzovaras D (2008) Intell Transp Syst IEEE Trans 9(3):491

    Article  Google Scholar 

  69. Shuyan H, Gangtie Z (2009) Expert Syst Appl 36:7651

    Article  Google Scholar 

  70. Balasubramanian V, Adalarasu K (2007) J Bodyw Mov Ther 11:151

    Article  Google Scholar 

  71. Katsis CD, Ntouvas NE, Bafas CG, Fotiadis DI (2004) Assessment of muscle fatigue during driving using surface emg. In: Proceedings of the IASTED international conference on biomedical engineering, Article, Innsbruck, pp 259–262

    Google Scholar 

  72. Fan X, Sun Y, Yin B, Guo X (2010) Pattern Recognit Lett 31:234

    Article  Google Scholar 

  73. Friedrichs F, Yang B (2010) Camera-based drowsiness reference for driver state classification under real driving conditions. In: 2010 IEEE intelligent vehicles symposium, San Diego, 2010

    Google Scholar 

  74. Pudil P, Ferrii FJ (1994) Floating search methods for feature selection with nonmonotonic criterion functions. In: Proceedings of the 12th international conference on pattern recognition, IAPR, Jerusalem, 1994

    Google Scholar 

  75. Sun XH, Xu L, Yang JY (2007) In: MIPPR 2007: automatic target recognition and image analysis; and multispectral image acquisition

    Google Scholar 

  76. Orazio TD, Leo M, Guaragnella C, Distante A (2007) Pattern Recognit 40(8):2341

    Article  Google Scholar 

  77. Suzuki M, Yamamoto N, Yamamoto O, Nakano T, Yamamoto S (2006) Systems, Man Cybern 4:2891

    Google Scholar 

  78. Senaratne R, Hardy D, Vander B, Halgamuge S (2007) Lect Notes Comput Sci 4492:801

    Article  Google Scholar 

  79. Rongben W, Lie G, Bingliang T, Lisheng J (2004) Monitoring mouth movement for driver fatigue or distraction with one camera. In: IEEE 7th conference on intelligent transportation systems, pp 314–319

    Google Scholar 

  80. Fan X, Yin BC, Sun YF (2007) Mach Learn Cybern 2:664

    Google Scholar 

  81. Vural E, Cetin M, Ercil A, Littlewort G, Bartlett M, Movellan J (2007) Drowsy driver detection through facial movement analysis. Springer, Berlin/Heidelberg

    Google Scholar 

  82. Kircher K, Ahlstrom C, Kircher A (2009) Comparison of two eye gaze based real-time driver distraction detection algorithms in a small-scale field operational test. In: Proceedings of the fifth international driving symposium on human factors in driver assessment, training and vehicle design, Big Sky, 2009

    Google Scholar 

  83. Pohl J, Birk W, Westervall L (2007) J Syst Control Eng 221(14)):541

    Google Scholar 

  84. Bergasa LM, Buenaposada JM, Nuevo J, Jimenez P, Baumela L (2008) Analysing driver’s attention level using computer vision. In: 11th international IEEE conference on intelligent transportation systems, Beijing

    Google Scholar 

  85. Furugori S, Yoshizawa N, Iname C, Miura Y (2005) Rev Automot Eng 26(1):53

    Google Scholar 

  86. Farid M, Kopf M, Bubb H, Essaili A (2006) Safety Lit 1960:639

    Google Scholar 

  87. Takei Y, Furukawa Y (2005) IEEE international conference on systems, Man Cybern 2:1765

    Google Scholar 

  88. Wakita T, Ozawa K, Miyajima C, Igarashi K, Itou K, Takeda K, Itakura F (2006) IEICE Trans Inf Syst E89-D(3):1188

    Article  Google Scholar 

  89. Torkkola K, Massey N, Wood C (2004) Driver inattention detection through intelligent analysis of readily available sensors. In: The 7th international IEEE conference on intelligent transportation systems, Washington, DC, 2004

    Google Scholar 

  90. Breiman L (2001) Random forests. Technical report, University of Califomia, Berkeley

    Google Scholar 

  91. Ersal T, Fuller HJA, Tsimhoni O, Stein JL, Fathy HK (2010) Model-based analysis and classification of driver distraction under secondary tasks. IEEE transactions on intelligent transportation systems 11(3):692–701

    Google Scholar 

  92. Sathyanarayana A, Nageswaren S, Ghasemzadeh H, Jafari R, Hansen JHL (2008) Body sensor networks for driver distraction identification. In: IEEE international conference on vehicular electronics and safety (ICVES), Columbus, 2008

    Google Scholar 

  93. Sathyanarayana A, Boyraz P, Hansen JHL (2011) Information fusion for robust ‘context and driver aware’ active vehicle safety systems. Inf Fusion 12(4):293–303

    Google Scholar 

  94. Doshi A, Trivedi M (2009) Investigating the relationships between gaze patterns, dynamic vehicle surround analysis, and driver intentions. In: Intelligent vehicles symposium. IEEE, Xi’an

    Google Scholar 

  95. Weller G, Schlag B (2009) In: European conference on human centred design for intelligent transport systems

    Google Scholar 

  96. Markkula G, Kutila M (2005) Online detection of driver distraction – preliminary results from the aide project. In: Proceedings of the 2005 international truck and bus safety and security symposium, Virginia, pp 86–96

    Google Scholar 

  97. Tango F, Calefato C, Minin L, Canovi L (2009) Moving attention from the road: a new methodology for the driver distraction evaluation using machine learning approaches. In: HSI 2009, Catania

    Google Scholar 

  98. Fletcher L, Zelinsky A (2007) Driver state monitoring to mitigate distraction. Technical report. In: Faulks IJ, Regan M, Stevenson M, Brown J, Porter A, Irwin JD (eds) Distracted driving. Sydney, NSW: Australasian College of Road Safety pp 487–523

    Google Scholar 

  99. Tran C, Trivedi MM (2010) Towards a vision-based system exploring 3d driver posture dynamics for driver assistance: issues and possibilities. In: 2010 IEEE intelligent vehicles symposium, San Diego, 2010

    Google Scholar 

  100. Land MF, Lee DN (1994) Nature 369:742

    Article  CAS  Google Scholar 

  101. Apostoloff N, Zelinsky A (2003) Int J Robot Res 5:28

    Google Scholar 

  102. Brandt T, Stemmer R, Rakotonirainy A (2004) IEEE Int Conf Syst Man Cybern 17:6451

    Google Scholar 

  103. Eren H, Celik U, Poyraz M (2007) Stereo vision and statistical based behavior prediction of driver. In: Proceedings of IEEE intelligent vehicles symposium, Istanbul, 2007

    Google Scholar 

  104. Su MC, Hsiung CY, Huang DY (2006) Syst Man Cybern 1:429

    Google Scholar 

  105. Ji Q, Yang XJ (2002) Real-Time Imaging 8:357

    Article  Google Scholar 

  106. Cudalbu C, Anastasiu B, Radu R, Cruceanu R, Schmidt E, Barth E (2005) Signals Circuits Syst 1:219

    Google Scholar 

  107. Huang H, Zhou YS, Zhang F, Liu FC (2007) Wavelet Anal Pattern Recognit 3:1144

    Google Scholar 

  108. Zhu ZW, Fujimura K, Ji Q (2007) Data Sci J 6:636

    Article  Google Scholar 

  109. Schmidt EA, Schrauf M, Simona M, Fritzsche M, Buchnerb A, Kincsesa WE (2009) Accid Anal Prev 41:1087

    Article  Google Scholar 

  110. Bouchner P (2006) WSEAS Trans Syst 5(1):84

    Google Scholar 

  111. Dong Y, Hu Z, Uchimura K, Murayama N (2010) A robust and efficient face tracking kernel for driver inattention monitoring system. In: Intelligent vehicles symposium (IV), 2010 IEE

    Google Scholar 

  112. Klauer SG, Dingus TA, Neale VL, Sudweeks JD, Ramsey DJ (2006) The impact of driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving study data. Technical report, National Highway Traffic Safety Administration, Washington, DC

    Google Scholar 

  113. Horne JA, Reyner LA (1995) Br Med J 310(6979):565

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Graduate School of Science and Technology, Kumamoto University, 2-39-1, Kurokami, Kumamoto, 860-8555, Japan

    Dr. Yanchao Dong

  2. Graduate School of Science and Technology, Computer Science Department, Kumamoto University, 2-39-1, Kurokami, Kumamoto, 860-8555, Japan

    Dr. Zhencheng Hu

Authors
  1. Dr. Yanchao Dong
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  2. Dr. Zhencheng Hu
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Correspondence to Yanchao Dong .

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  1. RAMTECH LIMITED, Larkspur, CA, USA

    Robert A. Meyers

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Dong, Y., Hu, Z. (2012). Driver Inattention Monitoring System for Intelligent Vehicles . In: Meyers, R.A. (eds) Encyclopedia of Sustainability Science and Technology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0851-3_787

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