JACIII Vol.19 No.5 pp. 611-618
doi: 10.20965/jaciii.2015.p0611


Using Brainwaves and Eye Tracking to Determine Attention Levels for Auto-Lighting Systems

Junzo Watada, Yung-Chin Hsiao, and Hanayuki Kitagawa

Graduate School of Information, Production, and Systems, Waseda University
2-7 Hibikino, Wakamatsu, Kitakyushu 808-0135, Japan

December 1, 2014
April 13, 2015
Online released:
September 20, 2015
September 20, 2015
eye tracking, brainwave, third braking light, daytime running light, light-emitting diode

To prevent car accidents, it should be possible for pedestrians and other drivers to detect oncoming vehicles. Many car accidents are caused because persons are not aware of approaching traffic, and this applies especially to visual awareness. The daytime running light (DRL) and the third braking light (TBL) were developed to significantly increase the visibility of vehicles, and their effectiveness has been verified through numerous studies. Usage of light-emitting diode (LED) lighting technology has also become popular in auto-lighting systems because of its advantages of energy efficiency, long life, and stylish appearance. However, LED lighting technology is very different from conventional incandescent or high-intensity discharge (HID) lighting technology. In this paper, we determine the effectiveness of LEDs as DRLs and TBLs. We measure human attention levels by observing brainwaves and performing eye-tracking experiments that shows the relationship between the theory of attention, brainwaves, and eye tracking. The results obtained show that it is feasible to evaluate automotive exterior lighting using the attention levels of subjects.

  1. [1]  C. M. Farmer, “Effectiveness estimates for center high mounted stop lamps: a six study,” Accident Analysis & Prevention, Vol.28, pp. 201-208, March 1996.
  2. [2]  H. Bar-Gera and E. Schechtman, “The effect of Center High Mounted Stop Lamp (CHMSL) on rear-end accident in Israel,” Accident Analysis and Prevention, Vol.37, pp. 531-536, 2005.
  3. [3]  K. Matthijs, B. Frits, and H. Marjan, “The Safety Effects of Daytime Running Lights,” SWOV Institute for Road Safety Research, The Netherlands, Leidschendam, 1997.
  4. [4]  B. Wordenweber, et al., “Automotive Lighting and Human Vision,” Springer-Verlag Berlin Heidelberg, 2007. ISBN: 978-3-540-36696-6.
  5. [5]  M. E. Krajicek and R. M. Schears, “Daytime running lights in the USA: what is the impact on vehicle crashes in Minnesota,” Int. J. of Emergency Medicine, Vol.3, No.1, pp. 39-43, Mar. 2010.
  6. [6]  N. Narendran, L. Deng, R. M. Pysar, Y. Gu, and H. Yu, “Performance characteristics of high power light emitting diodes,” 3rd Int. Conf. on Solid State Lighting, Proc. of SPIE 5187, pp. 267-275, 2004.
  7. [7]  Energy Savings Potential of Solid-State lighting in General Illumination Applications, Navigant Consulting Inc., 2012, available:
  8. [8]  M. Lay, “Handbook of Road Technology,” Traffic and Transport, Gordon and Breach Science Publishers, Vol.2, 1991.
  9. [9]  M. Paine, “A Review of Daytime Running Lights,” Sydney NRMA and RACV, July 2003. [Online] Available:
  10. [10]  Light Transmitting Glass Covers for Exterior Aircraft Lighting,
  11. [11]  AUDI’s new automotive lighting technolo- gies at CES 2013
  12. [12]  Adaptive Brake Light for Mercedes-Benz,
  13. [13]  Z. Li and P. Milgram, “An empirical investigation of a dynamic brake light concept for reduction of rear-end collisions through manipulation of optical looming,” Int. J. of Human-Computer Studies, Vol.66, No.3, pp. 158-172, Mar. 2008.
  14. [14]  I. Bukhman, “TRIZ Technology for Innovation,” Taipei, Cubic Creativity Company, 2012.
  15. [15]  Vehicle Code Section 24600-24617,
  16. [16]  J. R. Anderson, “Cognitive psychology and its implications (6th ed.),” Worth Publishers, pp. 519, 2004.
  17. [17]  D. Goleman, “Focus: The Hidden Driver of Excellence (Hardcover),” Harper, 2013.
  18. [18]  G. Ogrim, J. Kropotov, and K. Hestad, “The quantitative EEG theta/beta ratio in attention deficit/hyperactivity disorder and normal controls: Sensitivity, specificity, and behavioral correlates,” Psychiatry Research, Vol.198, No.3, pp. 482-488, Mar. 2012.
  19. [19]  G. Buzsaki, “Rhythms of the Brain,” New York, Oxford University Press, 2006.
  20. [20]
  21. [21]
  22. [22]  C. C. Wang and M. C. Hsu, “An exploratory study using inexpensive electroencephalography (EEG) to understand flow experience in computer-based instruction,” Information & Management, Vol.51, No.7, pp. 912-923, Nov. 2014.
  23. [23]  A. Nijholt, D.P.-O. Bos, and B. Reuderink, “Turning shortcomings into challenges: Brain-computer interfaces for games,” Entertainment Computing, Vol.1, No.2, pp. 85-94, Apr. 2009.
  24. [24]  A. L. Yarbus, “Eye Movements and Vision,” New York, Plenium Press, 1967.
  25. [25]  H. H. Lai, “The research & development of Kansei Engineering and Man-Machine interface safety for car-lighting styling design (I),” Taiwan, National Research Council Research, 2003.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Mar. 24, 2017