Analysis of braking intention based on fNIRS in driving simulation experiments

Analysis of braking intention based on fNIRS in driving simulation experiments

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Cooperative driving refers to a notion that advanced driver assistance system (ADAS) sharing the control with human driver and completing driving task together. One of the key technologies is that the ADAS can identify the driver's driving intention in real time to implement consistent driving decisions. Based on driving simulator (DS) and functional near-infrared spectroscopy (fNIRS) experiments, this study established a model of driver's brake intention identification with machine-learning algorithms and analysed cerebral cortex activity mechanism of the driver's brake intention with parametric test. This study suggested that the test accuracy of the model established here was 90.91%. Moreover, the activity in the Brodmann area 7 (BA7), BA17, and BA40 in cerebral cortex was significantly different between the driver with braking intention and those with driving at constant speed (p < 0.05). The study presented here not only identified the driver's driving intention through fNIRS for the first time, but also analysed the brain activity of the drivers when he had the braking intention. It lays a foundation for the future research on the driving cognitive model about human perception and driver's driving intention identification.


    1. 1)
      • 1. SAE international: ‘Taxonomy and definitions for terms related to driving automated systems for on-road motor vehicles’, 2016, J3016-201609.
    2. 2)
      • 2. Klingelschmitt, S., Platho, M., Gross, H.M., et al: ‘Combining behaviour and situation information for reliably estimating multiple intentions’. IEEE Intelligent Vehicles Symp. Proc., Dearborn, MI, USA, 2014, vol. 119, pp. 388393.
    3. 3)
      • 3. Jin, L., Hou, H., Jiang, Y.: ‘Driver intention recognition based on continuous hidden Markov model’. IEEE Int. Conf. on Transportation, Mechanical, and Electrical Engineering, Changchun, China, 2012, pp. 739742.
    4. 4)
      • 4. Dang, R., Wang, J., Li, K., et al: ‘Driver lane change characteristics for various highway driving conditions’, J. Tsinghua Univ., 2013, 53, (10), pp. 14811485.
    5. 5)
      • 5. Yoshino, K., Noriyuki, O., Kouji, Y., et al: ‘Correlation of prefrontal cortical activation with changing vehicle speeds in actual driving: a vector-based functional near-infrared spectroscopy study’, Front. Hum. Neurosci., 2013, 7, p. 895.
    6. 6)
      • 6. Ikenishi, T., Kamada, T.: ‘Estimation of driver's longitudinal intention for the preceding car by brain current distribution estimation method’. IEEE Int. Conf. on Intelligent Transportation Systems, Las Palmas, Spain, 2015, vol. 83, pp. 13111316.
    7. 7)
      • 7. Xin, X., Lu, X., Hou, Y., et al: ‘Vehicle stability control based on driver's emergency alignment intention recognition’, Int. J. Automot. Technol., 2017, 18, (6), pp. 9931006.
    8. 8)
      • 8. Schmidt, K., Beggiato, M., Hoffmann, K.H., et al: ‘A mathematical model for predicting lane changes using the steering wheel angle’, J. Safety Res., 2014, 49, (203), pp. 85.e185.e190.
    9. 9)
      • 9. Bocklisch, F., Bocklisch, S.F., Beggiato, M., et al: ‘Adaptive fuzzy pattern classification for the online detection of driver lane change intention’, Neurocomputing, 2017, 262, (1), pp. 148158.
    10. 10)
      • 10. Zhou, S., Wu, C.: ‘RETRACTED ARTICLE: a recognition method for driver's intention based on genetic algorithm and ant colony optimization’. Int. Conf. on Natural Computation, Shanghai, China, July 2011, vol. 2, pp. 10331037.
    11. 11)
      • 11. Zhang, R., Yan, X., Wu, C.: ‘A recognition model for acceleration intention of automobile drivers based on fuzzy clustering’. Int. Conf. on Transportation Information and Safety, Wuhan, China, 2011, pp. 19381947.
    12. 12)
      • 12. Xiong, L., Teng, G.W., Yu, Z.P., et al: ‘Novel stability control strategy for distributed drive electric vehicle based on driver operation intention’, Int. J. Automot. Technol., 2016, 17, (4), pp. 651663.
    13. 13)
      • 13. Diederichs, F., Schüttke, T., Spath, D.: ‘Driver intention algorithm for pedestrian protection and automated emergency braking systems’. IEEE Int. Conf. on Intelligent Transportation Systems, Las Palmas, Spain, 2015, pp. 10491054.
    14. 14)
      • 14. Su, C., Deng, W., Sun, H., et al: ‘Forward collision avoidance systems considering driver's driving behaviour recognized by Gaussian mixture model’. IEEE Intelligent Vehicles Symp., Los Angeles, CA, USA, 2017, pp. 535540.
    15. 15)
      • 15. Li, K., Wang, X., Xu, Y., et al: ‘Lane changing intention recognition based on speech recognition models ⋆’, Transp. Res. Part C Emerg. Technol., 2016, 69, pp. 497514.
    16. 16)
      • 16. Foy, H.J., Runham, P, Chapman, P.: ‘Prefrontal cortex activation and young driver behaviour: a fNIRS study’, PLoS One, 2016, 11, (5), p. e0156512.
    17. 17)
      • 17. Graydon, F.X., Young, R., Benton, M.D., et al: ‘Visual event detection during simulated driving: identifying the neural correlates with functional neuroimaging’, Transp. Res. Part F Traffic Psychol. Behav., 2004, 7, (4), pp. 271286.
    18. 18)
      • 18. Yoshino, K., Oka, N., Yamamoto, K., et al: ‘Functional brain imaging using near-infrared spectroscopy during actual driving on an expressway’, Front. Hum. Neurosci., 2013, 7, (882), p. 882.
    19. 19)
      • 19. Oka, N., Yoshino, K., Yamamoto, K., et al: ‘Greater activity in the frontal cortex on left curves: a vector-based fNIRS study of left and right curve driving’, PLoS One, 2015, 10, (5), p. e0127594.
    20. 20)
      • 20. Ikenishi, T., Kamada, T., Nagai, M.: ‘Analysis of longitudinal driving behaviours during car following situation by the driver's EEG using PARAFAC’, IFAC Proc., 2013, 46, (15), pp. 415422.
    21. 21)
      • 21. Ikenishi, T., Kamada, T.: ‘Estimation of driver's steering direction about lane change maneuver at the preceding car avoidance by brain source current estimation method’. IEEE Int. Conf. on Systems, Man and Cybernetics, San Diego, CA, USA, 2014, pp. 28082814.
    22. 22)
      • 22. Schweizer, T.A., Kan, K., Hung, Y., et al: ‘Brain activity during driving with distraction: an immersive fMRI study’, Front. Hum. Neurosci., 2013, 7, (3), p. 53.
    23. 23)
      • 23. Takahashi, N., Shimizu, S., Hirata, Y., et al: ‘Fundamental study for new assistive system based on brain activity during car driving’. IEEE Int. Conf. on Robotics and Biomimetics, Tianjin, China, 2010, pp. 745750.
    24. 24)
      • 24. Kato, T.: ‘Principle and technique of NIRS-imaging for human brain FORCE: fast-oxygen response in capillary event’. Int. Congress, Urayasu, Japan, 2004, vol. 1270, pp. 8590.
    25. 25)
      • 25. Orino, Y., Yamamoto, K., Oka, N., et al: ‘Relationship between brain activity and real-road driving behaviour: a vector-based whole-brain functional near-infrared spectroscopy study’. Driving Assessment Conf., Manchester Village, Vermont, 2017, pp. 1622.
    26. 26)
      • 26. Stoeckel, C., Gough, P.M., Watkins, K.E., et al: ‘Supramarginal gyrus involvement in visual word recognition’, Cortex, 2009, 45, (9), pp. 10911096.

Related content

This is a required field
Please enter a valid email address