Maykel M.P.G. Van Miltenburg
ABSTRACT
Driver distraction is a concern for traffic safety. Most research has been focused on validating or quantifying the relationship between eyes-off-road metrics and driving performance without specifically addressing cognitive aspects of distracted driving. The current study explores to what extent electroencephalogram data is a good predictor of how successful a distracted driver will be able to take over control from an autonomous vehicle. Participants were driving a simulated car while being exposed to varying levels of distraction. During the ride at several moments the participants were warned to take over control, after which the control was transferred. Sometimes after taking over the control an immediate break action of the drivers was expected. It turned out that electroencephalogram based data is able to indicate to what extent participants are distracted. However, electroencephalogram based data is not able to estimate driving performance during take over control.
- ERSO 2018. Driver Distraction. European Road Safety Observatory, https://road-safety.transport.ec.europa.eu/system/files/2021-07/ersosynthesis2018-driverdistraction.pdfGoogle Scholar
- Basacik, D., Reed, N. and Robbins, R. 2012. Smartphone use while driving: a simulator study.Google Scholar
- Collet, C., Guillot, A. and Petit, C. 2010. Phoning while driving II: a review of driving conditions influence. Ergonomics. 53, 5 (2010), 602–616.Google ScholarCross Ref
- Collet, C., Guillot, A. and Petit, C. 2010. Phoning while driving I: a review of epidemiological, psychological, behavioural and physiological studies. Ergonomics. 53, 5 (2010), 589–601.Google ScholarCross Ref
- Dingus, T.A., Guo, F., Lee, S., Antin, J.F., Perez, M., Buchanan-King, M. and Hankey, J. 2016. Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proceedings of the National Academy of Sciences. 113, 10 (2016), 2636–2641.Google ScholarCross Ref
- Stelling, A. and Hagenzieker, M.P. 2012. Afleiding in het verkeer: een overzicht van de literatuur. (2012).Google Scholar
- Regan, M.A., Lee, J.D. and Young, K. 2008. Driver distraction: Theory, effects, and mitigation. CRC press.Google Scholar
- Ranney, T.A., Garrott, W.R. and Goodman, M.J. 2001. NHTSA driver distraction research: Past, present, and future. Citeseer.Google Scholar
- Gabaude, C., Baracat, B., Jallais, C., Bonniaud, M. and Fort, A. 2012. Cognitive load measurement while driving. In: Human Factors: a view from an integrative perspective. Human Factors and Ergonomics Society.Google Scholar
- Wickens, C.D. 2008. Multiple resources and mental workload. Human factors. 50, 3 (2008), 449–455.Google Scholar
- Cantin, V., Lavallière, M., Simoneau, M. and Teasdale, N. 2009. Mental workload when driving in a simulator: Effects of age and driving complexity. Accident Analysis & Prevention. 41, 4 (2009), 763–771.Google ScholarCross Ref
- De Waard, D. and Brookhuis, K.A. 1996. The measurement of drivers’ mental workload. (1996).Google Scholar
- Miller, S. 2001. Workload Measures. National Advanced Driving Simulator. University of Iowa Press: Iowa City, IA, USA.Google Scholar
- Lee, J. and Yang, J.H. 2020. Analysis of Driver's EEG Given Take-Over Alarm in SAE Level 3 Automated Driving in a Simulated Environment. International Journal of Automotive Technology. 21, 3 (Jun. 2020), 719–728. DOI:https://doi.org/10.1007/s12239-020-0070-3.Google ScholarCross Ref
- Lew, R. 2014. Assessing cognitive workload from multiple physiological measures using wavelets and machine learning. University of Idaho.Google Scholar
- Käthner, I., Wriessnegger, S.C., Müller-Putz, G.R., Kübler, A. and Halder, S. 2014. Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain–computer interface. Biological Psychology. 102, (Oct. 2014), 118–129. DOI:https://doi.org/10.1016/j.biopsycho.2014.07.014.Google Scholar
- Klimesch, W. 2012. Alpha-band oscillations, attention, and controlled access to stored information. Trends in Cognitive Sciences. 16, 12 (Dec. 2012), 606–617. DOI:https://doi.org/10.1016/j.tics.2012.10.007.Google ScholarCross Ref
- Freeman, F.G., Mikulka, P.J., Prinzel, L.J. and Scerbo, M.W. 1999. Evaluation of an adaptive automation system using three EEG indices with a visual tracking task. Biological Psychology. 50, 1 (May 1999), 61–76. DOI:https://doi.org/10.1016/S0301-0511(99)00002-2.Google ScholarCross Ref
- Smith, A., Nutt, D., Wilson, S., Rich, N., Hayward, S. and Heatherley, S. 2002. Noise and Insomnia: A Study of Community Noise Exposure, Sleep Disturbance, Noise Sensitivity and Subjective Reports of Health: (556942009-001). American Psychological Association.Google Scholar
- Zhang, B., de Winter, J., Varotto, S., Happee, R. and Martens, M. 2019. Determinants of take-over time from automated driving: A meta-analysis of 129 studies. Transportation Research Part F: Traffic Psychology and Behaviour. 64, (Jul. 2019), 285–307. DOI:https://doi.org/10.1016/j.trf.2019.04.020.Google Scholar
- Fournier, L.R., Wilson, G.F. and Swain, C.R. 1999. Electrophysiological, behavioral, and subjective indexes of workload when performing multiple tasks: manipulations of task difficulty and training. International Journal of Psychophysiology. 31, 2 (Jan. 1999), 129–145. DOI:https://doi.org/10.1016/S0167-8760(98)00049-X.Google ScholarCross Ref
- Gevins, A. and Smith, M.E. 2003. Neurophysiological measures of cognitive workload during human-computer interaction. Theoretical Issues in Ergonomics Science. 4, 1–2 (Jan. 2003), 113–131. DOI:https://doi.org/10.1080/14639220210159717.Google ScholarCross Ref
Index Terms
- Can EEG Measurements be Used to Estimate the Performance of Taking over Control from an Autonomous Vehicle for Different Levels of Distracted Driving? An Explorative Study
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