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

access icon free Driving behaviour-based event data recorder

© The Institution of Engineering and Technology
Received 15/01/2013, Accepted 04/07/2013, Revised 08/05/2013, Published 19/12/2013

A general event data recorder is a device installed in automobiles to record information related to vehicle crashes or accidents. The data provide a better understanding of how certain crashes come about. This study made a prototype of a driving behaviour-based event data recorder (DBEDR), which provides the information of driving behaviours and a danger level. The authors approach is to recognise the seven behaviours: normal driving, acceleration, deceleration, changing to the left lane or right lane, zigzag driving and approaching the car in front by the hidden Markov models. All data were collected from a real vehicle and evaluated in a real road environment. The experimental results show that the proposed method achieved an average detection ratio of 95% for behaviour recognition. The danger level is inferred by fuzzy rules involved with the above behaviours. DBEDR recorded the recognised driving behaviours and the danger level, and the places were stored with the assistance of a global positioning system receiver. By integrating Google Maps, the locations, the driving behaviour occurrences, the danger level on the travel routes and the recorded images, the proposed DBEDR could be more useful compared with the traditional EDRs.

Inspec keywords: hidden Markov models; data recording; road safety; road accidents; traffic engineering computing; behavioural sciences computing

Other keywords: hidden Markov model; normal driving behaviour; zigzag driving behaviour; driving behaviour-based event data recorder; Google Maps; deceleration behaviour; vehicle crash; car approach behaviour; acceleration behaviour; lane change behaviour; automobile; DBEDR; vehicle accident

Subjects: Social and behavioural sciences computing; Data acquisition equipment and techniques; Markov processes; Traffic engineering computing

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
      • 22. Rabiner, L.R.: ‘A tutorial on hidden Markov models and selected applications in speech recognition’, Proc. IEEE, 1989, 77, (2), pp. 257–286 (doi: 10.1109/5.18626).
    18. 18)
      • 6. Ehlgen, T., Pajdla, T., Ammon, D.: ‘Eliminating blind spots for assisted driving’, IEEE Trans. Intell. Transp. Syst., 2008, 9, (4), pp. 657–665 (doi: 10.1109/TITS.2008.2006815).
    19. 19)
      • 3. Perez, A., Garcia, M.I., Nieto, M., Pedraza, J.L., Rodriguez, S., Zamorano, J.: ‘Argos: an advanced in-vehicle data recorder on a massively sensorized vehicle for car driver behavior experimentation’, IEEE Trans. Intell. Transp. Syst., 2010, 11, (2), pp. 463–473 (doi: 10.1109/TITS.2010.2046323).
    20. 20)
      • 11. Jap, B.T., Lal, S., Fischer, P., Bekiaris, E.: ‘Using EEG spectral components to assess algorithms for detecting fatigue’, Expert Syst. Appl., 2009, 36, (2, Part 1), pp. 2352–2359 (doi: 10.1016/j.eswa.2007.12.043).
    21. 21)
      • 4. Jain, J.J., Busso, C.: ‘Analysis of driver behaviors during common tasks using frontal video camera and CAN-bus information’. Proc. Multimedia and Expo (ICME), Barcelona, Spain, July 2011, pp. 1–6.
    22. 22)
      • 23. Paefgen, J., Kehr, F., Zhai, Y., Michahelles, F.: ‘Driving behavior analysis with smartphones: insights from a controlled field study’. Proc. Mobile and Ubiquitous Multimedia, Ulm, Germany, 2012, pp. 1–8.
    23. 23)
      • 12. Yeo, M.V.M., Li, X., Shen, K., Wilder-Smith, E.P.V.: ‘Can SVM be used for automatic EEG detection of drowsiness during car driving?’, Saf. Sci., 2009, 47, (1), pp. 115–124 (doi: 10.1016/j.ssci.2008.01.007).
    24. 24)
      • 10. Jiao, K., Li, Z., Chen, M., Wang, C., Qi, S.: ‘Effect of different vibration frequencies on heart rate variability and driving fatigue in healthy drivers’, Int. Arch. Occup. Environ. Health, 2004, 77, (3), pp. 205–212 (doi: 10.1007/s00420-003-0493-y).
    25. 25)
      • 14. Chua, C.P., McDarby, G., Heneghan, C.: ‘Combined electrocardiogram and photoplethysmogram measurements as an indicator of objective sleepiness’, Physiol. Meas., 2008, 29, pp. 857–868 (doi: 10.1088/0967-3334/29/8/001).
    26. 26)
      • 5. Toledo, T., Musicant, O., Lotan, T.: ‘In-vehicle data recorders for monitoring and feedback on drivers’ behavior’, Transp. Res. C, Emerg. Technol., 2008, 16, (3), pp. 320–331 (doi: 10.1016/j.trc.2008.01.001).
    27. 27)
      • 17. Zehang, S., Bebis, G., Miller, R.: ‘On-road vehicle detection: a review’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (5), pp. 694–711 (doi: 10.1109/TPAMI.2006.104).
    28. 28)
      • 7. Chen, Y.Y., Tu, Y.Y., Chiu, C.H., Chen, Y.S.: ‘An embedded system for vehicle surrounding monitoring’. Proc. Power Electronics and Intelligent Transportation System (PEITS), Shenzhen, December 2009, pp. 92–95.
    29. 29)
      • 8. Gandhi, T., Trivedi, M.M.: ‘Vehicle surround capture: survey of techniques and a novel omni-video-based approach for dynamic panoramic surround maps’, IEEE Trans. Intell. Transp. Syst., 2006, 7, (3), pp. 293–308 (doi: 10.1109/TITS.2006.880635).
    30. 30)
      • 9. Atsumi, B.: ‘Evaluation of mental condition on drivers by analysis of heart rate variability: measurement of mental stress and drowsiness by indexes of autonomic nervous system’, JSAE Rev., 1995, 16, (1), pp. 110–110 (doi: 10.1016/0389-4304(95)94851-D).
    31. 31)
      • 1. Gabler, H.C., Gabauer, D.J., Newell, H.L., O'Neill, M.E.: ‘Use of event data recorder (EDR) technology for highway crash data analysis’ (Transportation Research Board of the National Academies, 2005).
    32. 32)
      • 15. Lal, S.K.L., Craig, A.: ‘A critical review of the psychophysiology of driver fatigue’, Biol. Psychol., 2001, 55, (3), pp. 173–194 (doi: 10.1016/S0301-0511(00)00085-5).
    33. 33)
      • 18. McCall, J.C., Trivedi, M.M.: ‘Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation’, IEEE Trans. Intell. Transp. Syst., 2006, 7, (1), pp. 20–37 (doi: 10.1109/TITS.2006.869595).
    34. 34)
      • 21. Baum, L.E., Petrie, T.: ‘Statistical inference for probabilistic functions of finite state Markov chains’, Ann. Math. Stat., 1966, 37, (6), pp. 1554–1563 (doi: 10.1214/aoms/1177699147).
    35. 35)
      • 2. Hua, Y., Gang, D.: ‘A digital vehicle monitoring system based on 3G for public security’. Proc. Computer and Information Application (ICCIA), Tianjin, December 2010, pp. 146–148.
    36. 36)
      • 16. D'Orazio, T., Leo, M., Guaragnella, C., Distante, A.: ‘A visual approach for driver inattention detection’, Pattern Recogn., 2007, 40, (8), pp. 2341–2355 (doi: 10.1016/j.patcog.2007.01.018).
    37. 37)
      • 19. Chen, C.J., Peng, H.Y., Wu, B.F., Chen, Y.H.: ‘A real-time driving assistance and surveillance system’, J. Inf. Sci. Eng., 2009, 25, (5), pp. 1501–1523.
    38. 38)
      • 13. Shen, K.Q., Li, X.P., Ong, C.J., Shao, S.Y., Wilder-Smith, E.P.V.: ‘EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate’, Clin. Neurophysiol., 2008, 119, (7), pp. 1524–1533 (doi: 10.1016/j.clinph.2008.03.012).
    39. 39)
      • 20. Wu, B.F., Chen, Y.H., Kao, C.C., Li, Y.F., Chen, C.J.: ‘A vision-based collision warning system by surrounding vehicles detection’, KSII Trans. Internet Inf. Syst., 2012, 6, pp. 1203–1222.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2013.0009
Loading

Related content

content/journals/10.1049/iet-its.2013.0009
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address