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Driver Drowsiness Detection and Measurement Methods

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Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

This chapter provides an overview of driver drowsiness detection (DDD) and measurement methods and organizes them by category. The five main categories are: subjective, physiological, vehicle-based, behavioral, and hybrid. Most DDD systems being developed today rely on either vehicle-based measures—notably the steering wheel movement (SWM) and the standard deviation of lane position (SDLP)—or methods based on the detection of behavioral clues, e.g., closing of the eyes, yawning and nodding of the head.

Keywords

Driver Drowsiness Detection (DDD) Steering Wheel Movements DDD Systems Lane Position Fall Asleep 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    T. Abe, T. Nonomura, Y. Komada, S. Asaoka, T. Sasai, A. Ueno, and Y. Inoue. Detecting deteriorated vigilance using percentage of eyelid closure time during behavioral maintenance of wakefulness tests. International Journal of Psychophysiology, 82(3):269–274, 2011.CrossRefGoogle Scholar
  2. 2.
    T. Akerstedt, K. Hume, D. Minors, and J. Waterhouse. The subjective meaning of good sleep, an intraindividual approach using the Karolinska sleep diary. Percept Mot Skills, 79(1 Pt 1):287–96, 1994.CrossRefGoogle Scholar
  3. 3.
    M. Akin, M. B. Kurt, N. Sezgin, and M. Bayram. Estimating vigilance level by using EEG and EMG signals. Neural Comput. Appl., 17(3):227–236, Apr. 2008.CrossRefGoogle Scholar
  4. 4.
    H. J. Baek, G. S. Chung, K. K. Kim, and K.-S. Park. A smart health monitoring chair for nonintrusive measurement of biological signals. Information Technology in Biomedicine, IEEE Transactions on, 16(1):150–158, 2012.CrossRefGoogle Scholar
  5. 5.
    L. Bergasa, J. Nuevo, M. Sotelo, R. Barea, and M. Lopez. Real-time system for monitoring driver vigilance. Intelligent Transportation Systems, IEEE Transactions on, 7(1):63–77, 2006.CrossRefGoogle Scholar
  6. 6.
    D. J. Buysse, C. F. Reynolds III, T. H. Monk, S. R. Berman, and D. J. Kupfer. The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Research, 28:193–213, 1989.CrossRefGoogle Scholar
  7. 7.
    Carskadon MA and Dement WC and Mitler MM and Roth T and Westbrook PR and Keenan S. Guidelines for the multiple sleep latency test (MSLT): a standard measure of sleepiness. Sleep, 9:519–524, 1989.Google Scholar
  8. 8.
    B. Cheng, W. Zhang, Y. Lin, R. Feng, and X. Zhang. Driver drowsiness detection based on multisource information. Human Factors and Ergonomics in Manufacturing and Service Industries, 22(5):450–467, 2012.Google Scholar
  9. 9.
    E. Cheng, B. Kong, R. Hu, and F. Zheng. Eye state detection in facial image based on linear prediction error of wavelet coefficients. In Robotics and Biomimetics, 2008. ROBIO 2008. IEEE International Conference on, pages 1388–1392, 2009.Google Scholar
  10. 10.
    M. Dehnavi, N. Attarzadeh, and M. Eshghi. Real time eye state recognition. In Electrical Engineering (ICEE), 2011 19th Iranian Conference on, pages 1–4, 2011.Google Scholar
  11. 11.
    D. Dinges and U. S. N. H. T. S. Administration. Evaluation of Techniques for Ocular Measurement as an Index of Fatigue and as the Basis for Alertness Management. United States. Dept. of Transportation. National Highway Traffic Safety Administration, 1998.Google Scholar
  12. 12.
    W. Dong and P. Qu. Eye state classification based on multi-feature fusion. In Control and Decision Conference, 2009. CCDC ’09. Chinese, pages 231–234, 2009.Google Scholar
  13. 13.
    T. D’Orazio, M. Leo, C. Guaragnella, and A. Distante. A visual approach for driver inattention detection. Pattern Recognition, 40(8):2341–2355, 2007.CrossRefzbMATHGoogle Scholar
  14. 14.
    A. B. Douglass, R. Bornstein, G. Nino-Murcia, S. Keenan, L. Miles, V. P. Zarcone, C. Guilleminault, and W. C. Dement. The sleep disorders questionnaire. i: Creation and multivariate structure of SDQ. Sleep, 17(2):160–7, 1994.Google Scholar
  15. 15.
    S. H. Fairclough and R. Graham. Impairment of driving performance caused by sleep deprivation or alcohol: A comparative study. Human Factors: The Journal of the Human Factors and Ergonomics Society, 41(1):118–128, 1999.CrossRefGoogle Scholar
  16. 16.
    R. Feng, G. Zhang, and B. Cheng. An on-board system for detecting driver drowsiness based on multi-sensor data fusion using Dempster-Shafer theory. In Networking, Sensing and Control, 2009. ICNSC ’09. International Conference on Networking, pages 897–902, 2009.Google Scholar
  17. 17.
    J. Gomez-Clapers and R. Casanella. A fast and easy-to-use ECG acquisition and heart rate monitoring system using a wireless steering wheel. Sensors Journal, IEEE, 12(3):610–616, 2012.CrossRefGoogle Scholar
  18. 18.
    J. Guo and X. Guo. Eye state recognition based on shape analysis and fuzzy logic. In Intelligent Vehicles Symposium, 2009 IEEE, pages 78–82, 2009.Google Scholar
  19. 19.
    R. Hammoud, A. Wilhelm, P. Malawey, and G. Witt. Efficient real-time algorithms for eye state and head pose tracking in advanced driver support systems. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 2, pages 1181 vol. 2–, 2005.Google Scholar
  20. 20.
    P.-C. Hii and W.-Y. Chung. A comprehensive ubiquitous healthcare solution on an AndroidTM mobile device. Sensors, 11(7):6799–6815, 2011.CrossRefGoogle Scholar
  21. 21.
    E. Hoddes, V. Zarcone, H. Smythe, R. Phillips, and W. C. Dement. Quantification of sleepiness: a new approach. Psychophysiology, 10(4):431–6, 1973.CrossRefGoogle Scholar
  22. 22.
    T. Hong, H. Qin, and Q. Sun. An improved real time eye state identification system in driver drowsiness detection. In Control and Automation, 2007. ICCA 2007. IEEE International Conference on, pages 1449–1453, 2007.Google Scholar
  23. 23.
    J. A. Horne and L. A. Reyner. Sleep related vehicle accidents. BMJ, 310(6979):565–567, 3 1995.Google Scholar
  24. 24.
    S. Hu and G. Zheng. Driver drowsiness detection with eyelid related parameters by Support Vector Machine. Expert Syst. Appl., 36(4):7651–7658, May 2009.MathSciNetCrossRefGoogle Scholar
  25. 25.
    M. Ingre, T. Akerstedt, B. Peters, A. Anund, and G. Kecklund. Subjective sleepiness, simulated driving performance and blink duration: examining individual differences. Journal of Sleep Research, 15(1):47–53, 2006.CrossRefGoogle Scholar
  26. 26.
    C. Jiangwei, J. Lisheng, G. Lie, G. Keyou, and W. Rongben. Driver’s eye state detecting method design based on eye geometry feature. In Intelligent Vehicles Symposium, 2004 IEEE, pages 357–362, 2004.Google Scholar
  27. 27.
    M. Johns. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep, 14(6):540–5, 1991.Google Scholar
  28. 28.
    R. Khushaba, S. Kodagoda, S. Lal, and G. Dissanayake. Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. Biomedical Engineering, IEEE Transactions on, 58(1):121–131, 2011.CrossRefGoogle Scholar
  29. 29.
    R. Knipling, J. Wang, and M. J. Goodman. The role of driver inattention in crashes: New statistics from the 1995 crashworthiness data system. Annual proceedings of the Association for the Advancement of Automotive Medicine, 40:377–392, 1996.Google Scholar
  30. 30.
    A. Kokonozi, E. Michail, I. C. Chouvarda, and N. Maglaveras. A study of heart rate and brain system complexity and their interaction in sleep-deprived subjects. In Computers in Cardiology, 2008, pages 969–971, 2008.Google Scholar
  31. 31.
    M. B. Kurt, N. Sezgin, M. Akin, G. Kirbas, and M. Bayram. The ANN-based computing of drowsy level. Expert Systems with Applications, 36(2, Part 1):2534–2542, 2009.Google Scholar
  32. 32.
    B.-G. Lee and W.-Y. Chung. Multi-classifier for highly reliable driver drowsiness detection in Android platform. Biomedical Engineering: Applications, Basis and Communications, 24(02):147–154, 2012.Google Scholar
  33. 33.
    A. Lenskiy and J.-S. Lee. Driver’s eye blinking detection using novel color and texture segmentation algorithms. International Journal of Control, Automation and Systems, 10(2):317–327, 2012.CrossRefGoogle Scholar
  34. 34.
    W. C. Liang, J. Yuan, D. C. Sun, and M. H. Lin. Changes in physiological parameters induced by indoor simulated driving: Effect of lower body exercise at mid-term break. Sensors, 9(9):6913–6933, 2009.CrossRefGoogle Scholar
  35. 35.
    C.-C. Lien and P.-R. Lin. Drowsiness recognition using the Least Correlated LBPH. In Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2012 Eighth International Conference on, pages 158–161, 2012.Google Scholar
  36. 36.
    C.-T. Lin, C.-J. Chang, B.-S. Lin, S.-H. Hung, C.-F. Chao, and I.-J. Wang. A real-time wireless brain - computer interface system for drowsiness detection. Biomedical Circuits and Systems, IEEE Transactions on, 4(4):214–222, 2010.CrossRefGoogle Scholar
  37. 37.
    F.-C. Lin, L.-W. Ko, C.-H. Chuang, T.-P. Su, and C.-T. Lin. Generalized EEG-based drowsiness prediction system by using a self-organizing neural fuzzy system. IEEE Trans. on Circuits and Systems, 59-I(9):2044–2055, 2012.MathSciNetCrossRefGoogle Scholar
  38. 38.
    A. Liu, Z. Li, L. Wang, and Y. Zhao. A practical driver fatigue detection algorithm based on eye state. In Microelectronics and Electronics (PrimeAsia), 2010 Asia Pacific Conference on Postgraduate Research in, pages 235–238, 2010.Google Scholar
  39. 39.
    D. Liu, P. Sun, Y. Xiao, and Y. Yin. Drowsiness detection based on eyelid movement. In Education Technology and Computer Science (ETCS), 2010 Second International Workshop on, volume 2, pages 49–52, 2010.Google Scholar
  40. 40.
    J. Liu, C. Zhang, and C. Zheng. EEG-based estimation of mental fatigue by using KPCA–HMM and complexity parameters. Biomedical Signal Processing and Control, 5(2):124–130, 2010.MathSciNetCrossRefGoogle Scholar
  41. 41.
    W. Liu, Y. Wang, and L. Jia. An effective eye states detection method based on projection. In Signal Processing (ICSP), 2010 IEEE 10th International Conference on, pages 829–831, 2010.Google Scholar
  42. 42.
    Z. Liu and H. Ai. Automatic eye state recognition and closed-eye photo correction. In Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, pages 1–4, 2008.Google Scholar
  43. 43.
    A. T. McCartt, S. A. Ribner, A. I. Pack, and M. C. Hammer. The scope and nature of the drowsy driving problem in New York State. Accident Analysis and Prevention, 28(4):511–517, 1996.CrossRefGoogle Scholar
  44. 44.
    R. A. McKinley, L. K. McIntire, R. Schmidt, D. W. Repperger, and J. A. Caldwell. Evaluation of eye metrics as a detector of fatigue. Human Factors: The Journal of the Human Factors and Ergonomics Society, 53(4):403–414, 2011.CrossRefGoogle Scholar
  45. 45.
    M. M. Mitler, K. S. Gujavarty, and C. P. Browman. Maintenance of wakefulness test: a polysomnographic technique for evaluation treatment efficacy in patients with excessive somnolence. Electroencephalogr Clin Neurophysiol, 53(6):658–61, 1982.CrossRefGoogle Scholar
  46. 46.
    A. Mizuno, H. Okumura, and M. Matsumura. Development of neckband mounted active bio-electrodes for non-restraint lead method of ECG R wave. In J. Sloten, P. Verdonck, M. Nyssen, and J. Haueisen, editors, 4th European Conference of the International Federation for Medical and Biological Engineering, volume 22 of IFMBE Proceedings, pages 1394–1397. Springer Berlin Heidelberg, 2009.Google Scholar
  47. 47.
    E. Murphy-Chutorian and M. Trivedi. Head pose estimation and augmented reality tracking: An integrated system and evaluation for monitoring driver awareness. Intelligent Transportation Systems, IEEE Transactions on, 11(2):300–311, 2010.CrossRefGoogle Scholar
  48. 48.
    S. Otmani, T. Pebayle, J. Roge, and A. Muzet. Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers. Physiology and Behavior, 84(5):715–724, 2005.CrossRefGoogle Scholar
  49. 49.
    A. I. Pack, A. M. Pack, E. Rodgman, A. Cucchiara, D. F. Dinges, and C. Schwab. Characteristics of crashes attributed to the driver having fallen asleep. Accident Analysis and Prevention, 27(6):769–775, 1995.CrossRefGoogle Scholar
  50. 50.
    M. Patel, S. K. L. Lal, D. Kavanagh, and P. Rossiter. Applying neural network analysis on heart rate variability data to assess driver fatigue. Expert Syst. Appl., 38(6):7235–7242, June 2011.CrossRefGoogle Scholar
  51. 51.
    P. Philip, I. Ghorayeb, D. Leger, J. Menny, B. Bioulac, P. Dabadie, and C. Guilleminault. Objective measurement of sleepiness in summer vacation long-distance drivers. Electroencephalogr Clin Neurophysiol, 102(5):383–9, 1997.CrossRefGoogle Scholar
  52. 52.
    H. Qin, J. Liu, and T. Hong. An eye state identification method based on the Embedded Hidden Markov Model. In Vehicular Electronics and Safety (ICVES), 2012 IEEE International Conference on, pages 255–260, 2012.Google Scholar
  53. 53.
    C. Qingzhang, W. Wenfu, and C. Yuqin. Research on eye-state based monitoring for drivers’ dozing. Intelligent Information Technology Applications, 2007 Workshop on, 1:373–376, 2009.Google Scholar
  54. 54.
    L. Rosenthal, T. A. Roehrs, and T. Roth. The sleep-wake activity inventory: A self-report measure of daytime sleepiness. Biological Psychiatry, 34(11):810–820, 1993.CrossRefGoogle Scholar
  55. 55.
    M. Saradadevi and P. Bajaj. Driver fatigue detection using Mouth and Yawning analysis. International Journal of Computer Science and Network Security, 8(6):183–188, 2008.Google Scholar
  56. 56.
    P. Smith, M. Shah, and N. da Vitoria Lobo. Determining driver visual attention with one camera. Intelligent Transportation Systems, IEEE Transactions on, 4(4):205–218, 2003.CrossRefGoogle Scholar
  57. 57.
    P. Thiffault and J. Bergeron. Monotony of road environment and driver fatigue: a simulator study. Accident Analysis and Prevention, 35(3):381–391, 2003.CrossRefGoogle Scholar
  58. 58.
    Y.-l. Tian, T. Kanade, and J. F. Cohn. Eye-state action unit detection by Gabor Wavelets. In Proceedings of the Third International Conference on Advances in Multimodal Interfaces, ICMI ’00, pages 143–150, London, UK, UK, 2000. Springer-Verlag.Google Scholar
  59. 59.
    Z. Tian and H. Qin. Real-time driver’s eye state detection. In Vehicular Electronics and Safety, 2005. IEEE International Conference on, pages 285–289, 2005.Google Scholar
  60. 60.
    N. H. Villaroman and D. C. Rowe. Improving accuracy in face tracking user interfaces using consumer devices. In Proceedings of the 1st Annual conference on Research in information technology, RIIT ’12, pages 57–62, New York, NY, USA, 2012. ACM.Google Scholar
  61. 61.
    Volvo. Volvo driver alert control and lane departure warning system. http://www.zercustoms.com/news/Volvo-Driver-Alert-Control-and-Lane-Departure-Warning.html, 2007.
  62. 62.
    E. Vural. Video Based Detection of Driver Fatigue. PhD thesis, Sabanci University, 2009.Google Scholar
  63. 63.
    F. Wang, M. Zhou, and B. Zhu. A novel feature based rapid eye state detection method. In Robotics and Biomimetics (ROBIO), 2009 IEEE International Conference on, pages 1236–1240, 2009.Google Scholar
  64. 64.
    H. Wang, L. Zhou, and Y. Ying. A novel approach for real time eye state detection in fatigue awareness system. In Robotics Automation and Mechatronics (RAM), 2010 IEEE Conference on, pages 528–532, 2010.Google Scholar
  65. 65.
    M. E. Wewers and N. K. Lowe. A critical review of visual analogue scales in the measurement of clinical phenomena. Res Nurs Health, 13(4):227–36, 1990.CrossRefGoogle Scholar
  66. 66.
    Y.-S. Wu, T.-W. Lee, Q.-Z. Wu, and H.-S. Liu. An eye state recognition method for drowsiness detection. In Vehicular Technology Conference (VTC 2010-Spring), 2010 IEEE 71st, pages 1–5, 2010.Google Scholar
  67. 67.
    G. Yang, Y. Lin, and P. Bhattacharya. A driver fatigue recognition model based on information fusion and dynamic bayesian network. Information Sciences, 180(10):1942–1954, 2010. < ce:title > Special Issue on Intelligent Distributed Information Systems < /ce:title > .Google Scholar
  68. 68.
    X. Yu, U. of Minnesota. Intelligent Transportation Systems Institute, D. D. o. M. University of Minnesota, and I. Engineering. Real-time Nonintrusive Detection of Driver Drowsiness: Final Report. CTS (Series: Minneapolis, Minn.). Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2009.Google Scholar
  69. 69.
    X. Zhang, N. Zheng, F. Mu, and Y. He. Head pose estimation using isophote features for driver assistance systems. In Intelligent Vehicles Symposium, 2009 IEEE, pages 568–572, 2009.Google Scholar

Copyright information

© The Author(s) 2014

Authors and Affiliations

  1. 1.Department of Computer & Electrical EngineeringFlorida Atlantic UniversityBoca RatonUSA
  2. 2.Department of Computer & Electrical Engineering and Computer ScienceFlorida Atlantic UniversityBoca RatonUSA
  3. 3.Department of Computer Science & EngineeringFlorida Atlantic UniversityBoca RatonUSA

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