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Driver Drowsiness Detection pp 7–18Cite as

Driver Drowsiness Detection and Measurement Methods

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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.

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References

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. 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. 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. 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. 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. T. D’Orazio, M. Leo, C. Guaragnella, and A. Distante. A visual approach for driver inattention detection. Pattern Recognition, 40(8):2341–2355, 2007.

    Article  MATH  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. 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. P.-C. Hii and W.-Y. Chung. A comprehensive ubiquitous healthcare solution on an AndroidTM mobile device. Sensors, 11(7):6799–6815, 2011.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. J. A. Horne and L. A. Reyner. Sleep related vehicle accidents. BMJ, 310(6979):565–567, 3 1995.

    Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  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. M. Johns. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep, 14(6):540–5, 1991.

    Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. 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. 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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  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. 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. 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.

    Article  MathSciNet  Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. 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. 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. E. Vural. Video Based Detection of Driver Fatigue. PhD thesis, Sabanci University, 2009.

    Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  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. 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. 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. 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 

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Authors and Affiliations

  1. Department of Computer & Electrical Engineering, Florida Atlantic University, Boca Raton, FL, USA

    Aleksandar Čolić

  2. Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA

    Oge Marques

  3. Department of Computer Science & Engineering, Florida Atlantic University, Boca Raton, FL, USA

    Borko Furht

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  1. Aleksandar Čolić
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Čolić, A., Marques, O., Furht, B. (2014). Driver Drowsiness Detection and Measurement Methods. In: Driver Drowsiness Detection. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-11535-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-11535-1_2

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