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Effect of Driver`s Cognitive Distraction on Driver`s Physiological State and Driving Performance
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 Title & Authors
Effect of Driver`s Cognitive Distraction on Driver`s Physiological State and Driving Performance
Kim, Jun-Hoe; Lee, Woon-Sung;
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 Abstract
Objective: The aim of this study is to investigate effect of driver`s cognitive distraction on driver`s physiological state and driving performance, and then to determine parameters appropriate for detecting the cognitive distraction. Background: Driver distraction is a major cause of traffic accidents and poses a serious threat to traffic safety due to ever increasing use of in-vehicle information systems and mobile phones during driving. Cognitive distraction, among four different types of distractions, prevents a driver from processing traffic information correctly and adapting to change in surround vehicle behavior in time. However, the cognitive distraction is more difficult to detect because it normally does not involve significant change in driver behavior. Method: A full-scale driving simulator was used to create virtual driving environment and situations. Participants in the experiment drove the driving simulator in three different conditions: attentive driving with no secondary task, driving and conducting secondary task of adding numbers, and driving and conducting secondary task of conversing with an experimenter. Parameters related with driver`s physiological state and driving performance were measured and analyzed for their change. Results: The experiment results show that driver`s cognitive distraction, induced by secondary task of addition and conversation during driving, increased driver`s cognitive workload, and indeed brought change in driver`s physiological state and degraded driving performance. Conclusion: The galvanic skin response, pupil size, steering reversal rate, and driver reaction time are shown to be statistically significant for detecting cognitive distraction. The appropriate combination of these parameters will be used to detect the cognitive distraction and estimate risk of traffic accidents in real-time for a driver distraction warning system.
 Keywords
Cognitive distraction;Physiological state;Driving performance;Driving simulator;
 Language
English
 Cited by
 References
1.
Ahlstrom, C. and Kircher, K., "Review of real-time visual driver distraction detection algorithms", Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research, 2010.

2.
Azman, A., Meng, Q. and Edirisinghe, E., "Non intrusive physiologic almeasurement for driver cognitive distraction detection: Eye and mouth movements", 2010 3rd International Conference on Advanced Computer Theory and Engineering, 3(pp 595-599), 2010.

3.
Conesa, J., Electrodermal palmar asymmetry and nostril dominance, Perceptual and Motor Skills, 80(1), 211-216, 1995. crossref(new window)

4.
Dingus, T. A., Klauer, S. G., Neale, V. L., Petersen, A., Lee, S. E., Sudweeks, J., Perez, M. A., Hankey, J., Ramsey, D., Gupta, S., Bucher, C., Doerzaph, Z. R., Jermeland, J. and Knipling, R. R., The 100-Car Naturalistic Driving Study, Phase II - Results of the 100-Car Field Experiment, DOT HS 810 593, USDOT, 2006.

5.
Donmez, B., Boyle, L. N. and Lee, J. D., Safety implications of providing real-time feedback to distracted drivers, Accident Analysis & Prevention, 39(3), 581-590, 2007. crossref(new window)

6.
D'Orazioa, T., Leoa, M., Guaragnella, C. and Distante, A., A Visual Approach for Driver Inattention Detection, Pattern Recognition, 40(8), 2341-2355, 2007. crossref(new window)

7.
Lee, J. D., Young, K. L. and Regan, M. A., Defining driver distraction: Theory, Effects, and Mitigation, CRC Press, 2009.

8.
Liang, Y., Detecting Driver Distraction, Ph.D. Thesis, University of Iowa, 2009.

9.
Liang, Y., Reyes, M. L. and Lee, J. D., Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines, IEEE Transactions on Intelligent Transportation Systems, 8(2), 340-350, 2007. crossref(new window)

10.
Macdonald, W. A. and Hoffmann, E. R., Review of Relationships Between Steering Wheel Reversal Rate and Driving Task Demand, Human Factors, 22(6), 733-739, 1980.

11.
Mehler, B., Reimer, B., Coughlin, J. F. and Dusek, J. A., Impact of Incremental Increases in Cognitive Workload on Physiological Arousal and Performance in Young Adult Drivers, Transportation Research Board of the National Academies, 2138, 6-12, 2009. crossref(new window)

12.
Miller, G. A., The Magic Number Seven Plus or Minus Two: Some Limits on Our Capacity to Process Information, Psychological Review, 63(2), 81-97, 1956. crossref(new window)

13.
Miyaji, M., Kawanaka, H. and Oguri, K., "Effect of Pattern Recognition Features on Detection for Driver's Cognitive Distraction, 2010 13th International IEEE Conference on Intelligent Transportation Systems", (pp 605-610), 2010.

14.
Niedermeyer, E. and Lopes da Silva, F., Electroencephalography: Basic Principles, Clinical Applications and Related Fields, 5th ed., Lipincott Williams & Wilkins, 2005.

15.
Palinko, O., Kun, A. L., Shyrokov, A. and Heeman, P., "Estimating Cognitive Load Using Remote Eye Tracking in a Driving Simulator", Proceedings of the 2010 symposium on Eye-Tracking Research & Applications, (pp 141-144), 2010.

16.
Rakauskas, M. E., Gugerty, L. J. and Ward, N. J., Effects of naturalistic cell phone conversations on driving performance, Journal of Safety Research, 35(4), 453-464, 2004. crossref(new window)

17.
Ranney, T. A., Harbluk, J. L. and Noy, Y. I., Effects of voice technology on test track driving performance: Implications for driver distraction, Human Factors, 47(2), 439-454, 2005. crossref(new window)

18.
Road Traffic Authority, Traffic accident statistics analysis 2009, Road Traffic Authority, 2009.

19.
Rouder, J. N., Morey, R. D., Cowan, N., Zwilling, C. E., Morey, C. C. and Pratte, M. S., An Assessment of Fixed-Capacity Models of Visual Working Memory, Proceedings of the National Academy of Sciences of the United States of America, 105(16), 5975-5979, 2008. crossref(new window)

20.
Shamir, M., Eidelman, L. A., Floman, Y., Kaplan, L. and Pizov, R., Pulse Oximetry Plethysmographic Waveform during Changes in Blood Volume, British Journal of Anaesthesia, 82(2), 178-181, 1999. crossref(new window)

21.
Sheridan, T. B., Driver Distraction From a Control Theory Perspective, Human Factors: The Journal of the Human Factors and Ergonomics Society, 46(4), 587-599, 2004. crossref(new window)

22.
Su, M. C., Hsiung C. Y. and Huang, D. Y., "A Simple Approach to Implementing a System for Monitoring Driver Inattention", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, (pp 429-433), 2006.

23.
Sweller, J., Cognitive Load during Problem Solving: Effects on Learning, Cognitive Science, 12(2), 257-285, 1988. crossref(new window)

24.
Treat, J. R., Tumbas, N. S., McDonald, S. T., Shinar, D. and Hume, R. D., Tri-level study of the causes of traffic accidents, HS-034-3-535-77, USDOT, 1977.

25.
Yekhshatyan, L., Detecting distraction and degraded driver performance with visual behavior metrics, Ph.D. Thesis, University of Iowa, 2010.

26.
Yoshitsugu, N., Miki, Y., Ito, T. and Matsunaga, M., Study of Driver Distraction Due to Voice Interaction, Paper 2003-01-0127, SAE 2003.

27.
Zhang, H., Smith, M. and Dufour, R., A final report of safety vehicles using adaptive Interface Technology (Phase II: Task 7C: Visual Distraction, USDOT, 2008.