Abstract
Electric Vehicle (EV) is becoming a viable and popular option, but the acceptance of the technology can be challenging and lead to an elevated driving stress. The existing studies on stress of vehicle driving has been mainly limited to the non-EVs or survey analysis. In this research, EV driving data of 40 subjects is analyzed, where each subject was asked to drive an EV over a 53 km course in a suburban city of South Korea. Physiological data including electroencephalogram (EEG) and eye-gazing were obtained along with vehicle operational data such as state of charge, altitude, and speed. The dataset was rich in information, but individual difference and nonlinear patterns made it extremely difficult to draw meaningful insights. As a solution, an information-theoretic framework is proposed to evaluate mutual information between physiological and operational data as well as the entropy of physiological data itself. The result shows two groups of subjects, one not showing much evidence of stress and the other exhibiting sufficient stress. Among the subjects who showed sufficient driving stress, 9 out of the top 10 high EEG-entropy drivers were female, one driver showed a strong pattern of range anxiety, and several showed patterns of uphill climbing anxiety.
Similar content being viewed by others
References
Andreassi, J. L. (2013). Psychophysiology: Human Behavior & Physiological Response. Psychology Press. Mahwah, New Iersey, USA.
Brookhuis, K. A. and De Waard, D. (2010). Monitoring drivers’ mental workload in driving simulators using physiological measures. Accident Analysis & Prevention 42, 3, 898–903.
Cannon, J., Krokhmal, P. A., Chen, Y. and Murphey, R. (2012). Detection of temporal changes in psychophysiological data using statistical process control methods. Computer Methods and Programs in Biomedicine 107, 3, 367–381.
Cover, T. M. and Thomas, J. A. (2012). Elements of Information Theory. John Wiley & Sons. Hoboken, New Jersey, USA.
Eilebrecht, B., Wolter, S., Lem, J., Lindner, H. J., Vogt, R., Walter, M. and Leonhardt, S. (2012). The relevance of HRV parameters for driver workload detection in real world driving. Proc. IEEE Computing in Cardiology (CinC), Krakow, Poland, 409–412.
Franke, T., Neumann, I., Bühler, F., Cocron, P. and Krems, J. F. (2012). Experiencing range in an electric vehicle: Understanding psychological barriers. Applied Psychology 61, 3, 368–391.
Haufe, S., Kim, J. W., Kim, I. H., Sonnleitner, A., Schrauf, M., Curio, G. and Blankertz, B. (2014). Electrophysiologybased detection of emergency braking intention in realworld driving. J. Neural Engineering 11, 5, 056011.
Healey, J. A. and Picard, R. W. (2005). Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intelligent Transportation Systems 6, 2, 156–166.
Jeong, C., Lee, Y., Choi, S., Jung, D. and Lee, K. (2013). Comparison of driving characteristics between drivers in Korea and in the United States of America based on driver-vehicle interaction field database. Int. J. Automotive Technology 14, 1, 123–132.
Kawakita, E., Itoh, M. and Oguri, K. (2010). Estimation of driver's mental workload using visual information and heart rate variability. Proc. IEEE 13th Int. Conf. Intelligent Transportation Systems (ITSC), Funchal, Portugal, 765–769.
Kim, H. S., Hwang, Y., Yoon, D., Choi, W. and Park, C. H. (2014). Driver workload characteristics analysis using EEG data from an urban road. IEEE Trans. Intelligent Transportation Systems 15, 4, 1844–1849.
Kim, J. Y., Jeong, C. H., Jung, M. J., Park, J. H. and Jung, D. H. (2013). Highly reliable driving workload analysis using driver electroencephalogram (EEG) activities during driving. Int. J. Automotive Technology 14, 6, 965–970.
Kinney, J. B. and Atwal, G. S. (2014). Equitability, mutual information, and the maximal information coefficient. Proc. National Academy of Sciences 111, 9, 3354–3359.
Kraskov, A., Stögbauer, H. and Grassberger, P. (2004). Estimating mutual information. Physical Review E 69, 6, 066138.
Lee, B. G., Lee, B. L. and Chung, W. Y. (2014). Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals. Sensors 14, 10, 17915–17936.
Lee, S. K., Lee, S. M., Shin, T. and Han, M. (2017). Objective evaluation of the sound quality of the warning sound of electric vehicles with a consideration of the masking effect: Annoyance and detectability. Int. J. Automotive Technology 18, 4, 699–705.
Lee, S. M. and Lee, S. K. (2014). Objective evaluation of human perception of automotive sound based on physiological signal of human brain. Int. J. Automotive Technology 15, 2, 273–282.
Li, Z., Chen, L., Peng, J. and Wu, Y. (2017). Automatic detection of driver fatigue using driving operation information for transportation safety. Sensors 17, 6, 1212.
Nilsson, M. (2011). Electric Vehicles: The Phenomenon of Range Anxiety. ELVIRE. Sweden.
Rahimi-Eichi, H. and Chow, M. Y. (2014). Big-data framework for electric vehicle range estimation. Proc. IEEE 40th Annual Conf. Industrial Electronics Society, Dallas, Texas, USA, 5628–5634.
Rauh, N., Franke, T. and Krems, J. F. (2015). Understanding the impact of electric vehicle driving experience on range anxiety. Human Factors: J. Human Factors and Ergonomics Society 57, 1, 177–187.
SAE On-road Automated Vehicle Standards Committee (2014). Taxonomy and Definitions for Terms Related to On-road Motor Vehicle Automated Driving Systems. SAE Standard. J3016. 1–16.
Schmidt, E., Decke, R. and Rasshofer, R. (2016). Correlation between subjective driver state measures and psychophysiological and vehicular data in simulated driving. Proc. IEEE Intelligent Vehicles Symp. (IV), Gothenburg, Sweden, 1380–1385.
Shannon, C. E. (2001). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5, 1, 3–55.
Solovey, E. T., Zec, M., Garcia Perez, E. A., Reimer, B. and Mehler, B. (2014). Classifying driver workload using physiological and driving performance data: Two field studies. Proc. SIGCHI Conf. Human Factors in Computing Systems, Toronto, Ontario, Canada, 4057–4066.
Son, J., Park, M., Won, K., Kim, Y., Son, S., McGordon, A., Jennings, P. and Birrell, S. (2016). Comparative study between Korea and UK: Relationship between driving style and real-world fuel consumption. Int. J. Automotive Technology 17, 1, 175–181.
Visser, P. S., Krosnick, J. A. and Lavrakas, P. J. (2000). Handbook of Research Methods in Social and Personality Psychology. Cambridge University Press. New York, USA, 223–252.
Wang, M. S., Jeong, N. T., Kim, K. S., Choi, S. B., Yang, S. M., You, S. H., Lee, J. H. and Suh, M. W. (2016). Drowsy behavior detection based on driving information. Int. J. Automotive Technology 17, 1, 165–173.
Xing, Y., Lv, C., Cao, D., Wang, H. and Zhao, Y. (2018). Driver workload estimation using a novel hybrid method of error reduction ratio causality and support vector machine. Measurement, 114, 390–397.
Zhang, H., Chavarriaga, R., Khaliliardali, Z., Gheorghe, L., Iturrate, I. and Millán, J. (2015). EEG-based decoding of error-related brain activity in a real-world driving task. J. Neural Engineering 12, 6, 066028.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kim, S., Rhee, W., Choi, D. et al. Characterizing Driver Stress Using Physiological and Operational Data from Real-World Electric Vehicle Driving Experiment. Int.J Automot. Technol. 19, 895–906 (2018). https://doi.org/10.1007/s12239-018-0086-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-018-0086-0