Neural response based behavioral profiling of vehicle drivers to personalize alarm sequences, warn safety systems and trigger non-driver-in-loop control

Gowtham Gopalakrishnan Iyer; Ashok Vajravelu; Muhammad Mahadi

doi:10.53730/ijhs.v6nS4.10682

Authors

Gowtham Gopalakrishnan Iyer
gowtham.g.iyer@gmail.com
Research Scholar, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia
Ashok Vajravelu Senior Lecturer, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia
Muhammad Mahadi Associate Professor, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia

Keywords:

BCI (brain-computer-interface), B2V (brain-to-vehicle), collision avoidance, ERP (event-related-potentials), feature extraction

Abstract

Quantitative measurement of vehicle driver alertness and response readiness to on-road emergency cues could add more response time to driver assistance and safety features. Exogenous Electroencephalogram (EEG) potentials generated in Beta frequencies can imply driver’s attention to the situation, the absence of which may be associated with distraction or drowsiness. Detection of specific Alpha potentials during action-soliciting events may further indicate risk of violating response-trigger thresholds beyond which physical and vehicular response periods may become too short to avoid collision. Specific Event-Related Potentials (ERP) associated with early automatic cognitive process of consciousness to obstacles, emotional reaction to visual cues, risk evaluation, drier’s emotional intensity, anticipation and motor preparation can be identified, isolated and processed to create a model that predicts driver’s intension/capability to respond to obstacles within a derived time threshold. Isolating signals that indicate driver in-activity (extreme fatigue, sleep, visual or auditory distraction) during near-obstacle situations could further be used to preempt user-in-loop drive, in semi-autonomous vehicles, even if the driver is inhibiting normal transition to self-drive mode. Existing safety components, like airbags, could be issued pre-warnings about possible crash situations (currently they are actuated only upon impact), giving them critical additional time to get pre-actuation sequences triggered.

Downloads

Download data is not yet available.

References

Brown, S. B., van Steenbergen, H., Band, G. P., de Rover, M., & Nieuwenhuis, S. (2012). Functional significance of the emotion-related late positive potential. Frontiers in human neuroscience, 6, 33.

Carretié, L., Iglesias, J., Garcia, T., & Ballesteros, M. (1997). N300, P300 and the emotional processing of visual stimuli. Electroencephalography and clinical Neurophysiology, 103(2), 298-303.

Carretié, L., Mercado, F., Tapia, M., & Hinojosa, J. A. (2001). Emotion, attention, and the ‘negativity bias’, studied through event-related potentials. International journal of psychophysiology, 41(1), 75-85.

Chavarriaga, R., Uscumlic, M., Zhang, H., Khaliliardali, Z., Aydarkhanov, R., Saeedi, S., & Millán, J. D. R. (2018). Decoding neural correlates of cognitive states to enhance driving experience. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(ARTICLE), 288-297.

Correll, J., Urland, G. R., & Ito, T. A. (2006). Event-related potentials and the decision to shoot: The role of threat perception and cognitive control. Journal of Experimental Social Psychology, 42(1), 120-128.

Delivers more excitement and driving pleasure by detecting, analyzing and responding to driver's brainwaves in real time. (2018). Retrieved from https://www.nissan-global.com/EN/INNOVATION/TECHNOLOGY/ARCHIVE/B2V/

Gharagozlou, F., Saraji, G. N., Mazloumi, A., Nahvi, A., Nasrabadi, A. M., Foroushani, A. R., & Samavati, M. (2015). Detecting driver mental fatigue based on EEG alpha power changes during simulated driving. Iranian journal of public health, 44(12), 1693.

Huang, Y. X., & Luo, Y. J. (2006). Temporal course of emotional negativity bias: an ERP study. Neuroscience letters, 398(1-2), 91-96.

Igliński, H., & Babiak, M. (2017). Analysis of the potential of autonomous vehicles in reducing the emissions of greenhouse gases in road transport. Procedia engineering, 192, 353-358.

Ilková, V., & Ilka, A. (2017, June). Legal aspects of autonomous vehicles—an overview. In 2017 21st international conference on process control (PC) (pp. 428-433). IEEE.

Ito, T. A., Larsen, J. T., Smith, N. K., & Cacioppo, J. T. (1998). Negative information weighs more heavily on the brain: the negativity bias in evaluative categorizations. Journal of personality and social psychology, 75(4), 887.

Li, N., Jain, J. J., & Busso, C. (2013). Modeling of driver behavior in real world scenarios using multiple noninvasive sensors. IEEE transactions on multimedia, 15(5), 1213-1225.

Liu, N. H., Chiang, C. Y., & Chu, H. C. (2013). Recognizing the degree of human attention using EEG signals from mobile sensors. sensors, 13(8), 10273-10286.

Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018). A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. Journal of neural engineering, 15(3), 031005.

Ma, Q., Bai, X., Pei, G., & Xu, Z. (2018). The hazard perception for the surrounding shape of warning signs: evidence from an event-related potentials study. Frontiers in neuroscience, 12, 824.

Ma, Q., Jin, J., & Wang, L. (2010). The neural process of hazard perception and evaluation for warning signal words: evidence from event-related potentials. Neuroscience letters, 483(3), 206-210.

Martínez-Díaz, M., & Soriguera, F. (2018). Autonomous vehicles: theoretical and practical challenges. Transportation Research Procedia, 33, 275-282.

Milakis, D., Snelder, M., Van Arem, B., Van Wee, B., & de Almeida Correia, G. H. (2017). Development and transport implications of automated vehicles in the Netherlands: Scenarios for 2030 and 2050. European Journal of Transport and Infrastructure Research, 17(1).

Munoz, F., & Martin-Loeches, M. (2015). Electrophysiological brain dynamics during the esthetic judgment of human bodies and faces. Brain Research, 1594, 154-164.

Pattinson, J. A., Chen, H., & Basu, S. (2020). Legal issues in automated vehicles: critically considering the potential role of consent and interactive digital interfaces. Humanities and Social Sciences Communications, 7(1), 1-10.

Primadewi, K., & Biomi, A. A. (2021). Effect of occupational health safety on medical staff performance in Bali Royal Hospital Denpasar. International Journal of Health & Medical Sciences, 4(1), 141-144. https://doi.org/10.31295/ijhms.v4n1.1642

Rozhkova, N., Rozhkova, D., & Blinova, U. (2020, May). An overview of aspects of autonomous vehicles’ development in digital era. In International Conference on Integrated Science (pp. 313-324). Springer, Cham.

Stern, R. E., Chen, Y., Churchill, M., Wu, F., Delle Monache, M. L., Piccoli, B., & Work, D. B. (2019). Quantifying air quality benefits resulting from few autonomous vehicles stabilizing traffic. Transportation Research Part D: Transport and Environment, 67, 351-365.

Tan, D., & Nijholt, A. (2010). Brain-computer interfaces and human-computer interaction. In Brain-Computer Interfaces (pp. 3-19). Springer, London.

Walter, W., Cooper, R., Aldridge, V. J., McCallum, W. C., & Winter, A. L. (1964). Contingent negative variation: an electric sign of sensori-motor association and expectancy in the human brain. nature, 203(4943), 380-384.

Widana, I.K., Sumetri, N.W., Sutapa, I.K., Suryasa, W. (2021). Anthropometric measures for better cardiovascular and musculoskeletal health. Computer Applications in Engineering Education, 29(3), 550–561. https://doi.org/10.1002/cae.22202