Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon openaccess Real-time eye tracking for the assessment of driver fatigue

Eye-tracking is an important approach to collect evidence regarding some participants’ driving fatigue. In this contribution, the authors present a non-intrusive system for evaluating driver fatigue by tracking eye movement behaviours. A real-time eye-tracker was used to monitor participants’ eye state for collecting eye-movement data. These data are useful to get insights into assessing participants’ fatigue state during monotonous driving. Ten healthy subjects performed continuous simulated driving for 1–2 h with eye state monitoring on a driving simulator in this study, and these measured features of the fixation time and the pupil area were recorded via using eye movement tracking device. For achieving a good cost-performance ratio and fast computation time, the fuzzy K-nearest neighbour was employed to evaluate and analyse the influence of different participants on the variations in the fixation duration and pupil area of drivers. The findings of this study indicated that there are significant differences in domain value distribution of the pupil area under the condition with normal and fatigue driving state. Result also suggests that the recognition accuracy by jackknife validation reaches to about 89% in average, implying that show a significant potential of real-time applicability of the proposed approach and is capable of detecting driver fatigue.

References

    1. 1)
      • 31. Hemadri, V.B., Kulkarni, U.P.: ‘Detection of drowsiness using fusion of yawning and eyelid movements’, Commun. Comput. Inf. Sci., 2013, 361, pp. 583594.
    2. 2)
      • 32. Fan, X., Sun, Y., Yin, B.: ‘Driver fatigue detection based on AdaBoost global features’, J. Comput. Inf. Syst., 2009, 5, (1), pp. 6168.
    3. 3)
      • 36. Hu, J.F.: ‘Comparison of different features and classifiers for driver fatigue detection based on a single EEG channel’, Comput. Math. Methods Med., 2017, 2017, (3), pp. 19, doi: 10.1155/2017/5109530.
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
      • 9. Lang, L., Qi, H.: ‘The study of driver fatigue monitor algorithm combined PERCLOS and AECS’. Int. Conf. on Computer Science and Software Engineering, 2008, pp. 349352.
    11. 11)
      • 25. Choi, I.H., Hong, S.K., Kim, Y.G.: ‘Real-time categorization of driver's gaze zone using the deep learning techniques’. Int. Conf. Big Data and Smart Computing, 2016, pp. 143148.
    12. 12)
    13. 13)
    14. 14)
      • 10. Dong, Y., Hu, Z., Uchimura, K., et al: ‘Driver inattention monitoring system for intelligent vehicles: a review’. Intelligent Vehicles Symp., 2009, pp. 875880.
    15. 15)
    16. 16)
      • 4. Saini, V., Saini, R.: ‘Driver drowsiness detection system and techniques: a review’, Int. J. Comput. Sci. Inf. Technol., 2014, 5, (3), pp. 42454249.
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
      • 5. Li, D.H., Liu, Q., Yuan, W., et al: ‘Relationship between fatigue driving and traffic accident’, J. Traffic Transp. Eng., 2010, 2, pp. 104109.
    22. 22)
      • 23. Jing-Jing, L.V.: ‘Fatigue recognition based on adaptive locality preserving projections’, Comput. Eng. Appl., 2010, 46, (22), pp. 187189.
    23. 23)
    24. 24)
      • 24. Lin, L., Huang, C., Ni, X., et al: ‘Driver fatigue detection based on eye state’, Technol. Health Care Off. J. Eur. Soc. Eng. Med., 2015, 23, (s2), pp. S453S463.
    25. 25)
    26. 26)
      • 6. Wang, Q., Yang, J., Ren, M.: ‘Driver fatigue detection: a survey’. The Sixth World Congress on IEEE Intelligent Control and Automation, Dalian, China, June 2006, vol. 2, pp. 85878591.
    27. 27)
    28. 28)
      • 7. Schmidt, E., Decke, R., Rasshofer, R.: ‘Correlation between subjective driver state measures and psychophysiological and vehicular data in simulated driving’. Intelligent Vehicles Symp., Gothenburg, Sweden, June 2016, pp. 13801385.
    29. 29)
    30. 30)
      • 21. Vapnik, V.: ‘Estimation of dependences based on empirical data’ (Springer, New York, NY, 2006).
    31. 31)
    32. 32)
    33. 33)
    34. 34)
      • 11. Trutschel, U., Sirois, B., Sommer, D., et al: ‘PERCLOS: an alertness measure of the past’. Driving Assessment 2011: 6th Int. Driving Symp. Human Factors in Driver Assessment, Training, and Vehicle Design, 2011.
    35. 35)
      • 29. Punitha, A., Geetha, M.K., Sivaprakash, A.: ‘Driver fatigue monitoring system based on eye state analysis’. Int. Conf. Circuit, Nagercoil, India, March 2014, pp. 14051408.
    36. 36)
    37. 37)
http://iet.metastore.ingenta.com/content/journals/10.1049/htl.2017.0020
Loading

Related content

content/journals/10.1049/htl.2017.0020
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address