Skip to main content
SpringerLink
Log in

Modeling Individual Differences in Driver Workload Inference Using Physiological Data

  • Published:
International Journal of Automotive Technology Aims and scope Submit manuscript

Abstract

Inferring driver workload has started to draw greater attention with the emerging automotive technology of higher autonomy. In this paper, we revisited the popular assumption of fixed workload levels determined by the driving environment, and propose a framework to generate a Personalized Driver Workload Profile (PDWP) that incorporates individual differences. A rich set of physiological and operational data from a real-traffic Electric Vehicle (EV) driving experiment was utilized. Physiological features were generated and selected from forty drivers’ electroencephalogram (EEG) and electrocardiogram (ECG) signals using multiple signal processing and machine learning techniques. A PDWP is defined as a random variable with three possible workload levels, and conditional distributions of the PDWP of the rest period and four driving environments were generated using fuzzy c-means clustering. The results revealed there exists little resemblance among the PDWPs of individual drivers, even in an identical driving environment. Moreover, some drivers exhibited strong evidence of EV range stress, but such phenomena were not universal. Our study is the first attempt to incorporate individual differences in estimating driving workload based on the direct cognitive responses using physiological data collected in a real-traffic experiment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Andreassi, J. L. (2013). Psychophysiology: human behavior & physiological response. 4th edn. Psychology Press. Hove, East Sussex, England.

    Book  Google Scholar 

  • Bender, A., Agamennoni, G., Ward, J. R., Worrall, S. and Nebot, E. M. (2015). An unsupervised approach for inferring driver behavior from naturalistic driving data. IEEE Trans. Intelligent Transportation Systems 16,6, 3325–3336.

    Article  Google Scholar 

  • Benedetto, S., Pedrotti, M., Minin, L., Baccino, T., Re, A. and Montanari, R. (2011). Driver workload and eye blink duration. Transportation research part F: traffic psychology and behaviour 14,3, 199–208.

    Article  Google Scholar 

  • Bezdec, J. C. (1981). Pattern recognition with Fuzzy objective function algorithms. Plenum Press. New York, NY, USA.

    Book  Google Scholar 

  • Chen, L. L., Zhao, Y., Ye, P. F., Zhang, J. and Zou, J. Z. (2017). Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers. Expert Systems with Applications, 85, 279–291.

    Article  Google Scholar 

  • Chiang, H. S. (2015). ECG-based mental stress assessment using fuzzy computing and associative petri net. J. Medical and Biological Engineering 35,6, 833–844.

    Article  Google Scholar 

  • Cinaz, B., Arnrich, B., Marca, R. and Tröster, G. (2013). Monitoring of mental workload levels during an everyday life office-work scenario. Personal and Ubiquitous Computing 17,2, 229–239.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hajek, W., Gaponova, I., Fleischer, K. H. and Krems, J. (2013). Workload-adaptive cruise control-A new generation of advanced driver assistance systems. Transportation Research Part F: Traffic Psychology and Behaviour, 20, 108–120.

    Article  Google Scholar 

  • Harbluk, J. L., Noy, Y. I., Trbovich, P. L. and Eizenman, M. (2007). An on-road assessment of cognitive distraction: Impacts on drivers’ visual behavior and braking performance. Accident Analysis & Prevention 39,2, 372–379.

    Article  Google Scholar 

  • He, L., Hu, D., Wan, M., Wen, Y., Von Deneen, K. M. and Zhou, M. (2015). Common Bayesian network for classification of EEG-based multiclass motor imagery BCI. IEEE Trans. Systems, Man, and Cybernetics: Systems 46,6, 843–854.

    Article  Google Scholar 

  • Healey, J. and Picard, R. W. (2005). Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intelligent Transportation Systems 6,2, 156–166.

    Article  Google Scholar 

  • Ho, K. K., Moody, G. B., Peng, C. K., Mietus, J. E., Larson, M. G., Levy, D. and Goldberger, A. L. (1997). Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation 96,3, 842–848.

    Article  Google Scholar 

  • Jackson, J. E. (1991). A user’s guide to principal components. John Wiley and Sons. New York, NY, USA.

    Book  MATH  Google Scholar 

  • Jamson, A. H., Merat, N., Carsten, O. M. and Lai, F. C. (2013). Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions. Transportation Research Part C: Emerging Technologies, 30, 116–125.

    Article  Google Scholar 

  • Kendall, M. G. (1970). Rank correlation methods. 4th edn. Charles Griffin & Co. London, UK.

    MATH  Google Scholar 

  • Khan, M. J. and Hong, K. S. (2015). Passive BCI based on drowsiness detection: an fNIRS study. Biomedical Optics Express 6,10, 4063–4078.

    Article  Google Scholar 

  • Kim, J. Y., Jeong, C. H., Woo, J. M., Jeong, M. J., Park, J. H. and Jung, D. H. (2011). Driving workload analysis using physiological signal in highway. Korean Society for Automotive Engineers, 2117–2123.

  • Kim, S., Rhee, W., Choi, D., Jang, Y. J. and Yoon, Y. (2018). Characterizing driver stress using physiological and operational data from real-world electric vehicle driving experiment. Int. J. Automotive Technology 19,5, 895–906.

    Article  Google Scholar 

  • Kumar, M., Arndt, D., Kreuzfeld, S., Thurow, K., Stoll, N. and Stoll, R. (2008). Fuzzy techniques for subjective workload-score modeling under uncertainties. IEEE Trans. Systems, Man, and Cybernetics, Part B (Cybernetics) 38,6, 1449–1464.

    Article  Google Scholar 

  • Kumar, M., Weippert, M., Vilbrandt, R., Kreuzfeld, S. and Stoll, R. (2007). Fuzzy evaluation of heart rate signals for mental stress assessment. IEEE Trans. Fuzzy Systems 15,5, 791–808.

    Article  Google Scholar 

  • Kwon, O. H., Rhee, W. and Yoon, Y. (2015). Application of classification algorithms for analysis of road safety risk factor dependencies. Accident Analysis & Prevention, 75, 1–15.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lim, J. B., Lee, S. B., Kim, K. H., Kim, S. Y. and Choi, J. S. (2012). A study of the relationship between driver’s anxiety eeg & driving speed in motorway sections. J. Korean Society of Safety 27,3, 167–175.

    Google Scholar 

  • Nilsson, M. (2011). Electric vehicles: The phenomenon of range anxiety. Report for the ELVIRE Project (FP7 PROJECT ID: ICT-2009.6. 1).

  • Ohn-Bar, E. and Trivedi, M. M. (2016). Looking at humans in the age of self-driving and highly automated vehicles. IEEE Trans. Intelligent Vehicles 1,1, 90–104.

    Article  Google Scholar 

  • Patten, C. J., Kircher, A., Östlund, J., Nilsson, L. and Svenson, O. (2006). Driver experience and cognitive workload in different traffic environments. Accident Analysis & Prevention 38,5, 887–894.

    Article  Google Scholar 

  • Pincus, S. M. and Goldberger, A. L. (1994). Physiological time-series analysis: what does regularity quantify?. American J. Physiology-Heart and Circulatory Physiology 266,4, H1643–H1656.

    Article  Google Scholar 

  • Plarre, K., Raij, A., Hossain, S. M., Ali, A. A., Nakajima, M., Al’Absi, M. and Siewiorek, D. (2011). Continuous inference of psychological stress from sensory measurements collected in the natural environment. Proc. 10th ACM/IEEE Int. Conf. Information Processing in Sensor Networks (IPSN). Chicago, IL, USA.

  • Ranganathan, G., Rangarajan, R., Bindhu, V. (2012). Estimation of heat rate signals for mental stress assessment using neuro fuzzy technique. Applied Soft Computing, 12, 1978–1984.

    Article  Google Scholar 

  • Rauh, N., Franke, T. and Krems, J. F. (2015). Understanding the impact of electric vehicle driving experience on range anxiety. Human Factors 57,1, 177–187.

    Article  Google Scholar 

  • Rauh, N., Franke, T. and Krems, J. F. (2017). First-time experience of critical range situations in BEV use and the positive effect of coping information. Transportation Research Part F: Traffic Psychology and Behaviour, 44, 30–41.

    Article  Google Scholar 

  • Richman, J. S. and Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American J. Physiology-Heart and Circulatory Physiology 278,6, H2039–H2049.

    Article  Google Scholar 

  • SAE On-Road Automated Vehicle Standards Committee. (2014). Taxonomy and definitions for terms and related to on-road motor vehicle automated driving systems. SAE Standard J., 3016, 1–16.

    Google Scholar 

  • Schießl, C. (2007). Stress and strain while driving. Proc. Young Researchers Seminar-European Conference of Transport Research Institutes (ECTRI). Brno, Czech Republic.

  • Setz, C., Arnrich, B., Schumm, J., La Marca, R., Tröster, G. and Ehlert, U. (2009). Discriminating stress from cognitive load using a wearable EDA device. IEEE Trans. Information Technology in Biomedicine 14,2, 410–417.

    Article  Google Scholar 

  • Shi, B., Xu, L., Hu, J., Tang, Y., Jiang, H., Meng, W. and Liu, H. (2015). Evaluating driving styles by normalizing driving behavior based on personalized driver modeling. IEEE Trans. Systems, Man, and Cybernetics: Systems 45,12, 1502–1508.

    Article  Google Scholar 

  • Shi, Y., Nguyen, M. H., Blitz, P., French, B., Fisk, S., De la Torre, F., Smailagic, A., Siewiorek, D. P., Al’Absi, M., Ertin, E., Kamarck, T. and Kumar, S. (2010). Personalized stress detection from physiological measurements. Int. Symp. Quality of Life Technology.

  • Stuiver, A., Brookhuis, K.A., de Waard, D., Mulder, B. (2014). Short-term cardiovascular measures for driver support: increasing sensitivity for detecting changes in mental workload. Int. J. Psychophysiology 92,1, 35–41.

    Article  Google Scholar 

  • Thatcher, A. (2013). Green ergonomics: definition and scope. Ergonomics 56,3, 389–398.

    Article  Google Scholar 

  • Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Trans. Neural Networks 10,5, 988–999.

    Article  Google Scholar 

  • Vicente, J., Laguna, P., Bartra, A. and Bailón, R. (2016). Drowsiness detection using heart rate variability. Medical & Biological Engineering & Computing 54,6, 927–937.

    Article  Google Scholar 

  • Vollmer, M. (2015). A robust, simple and reliable measure of heart rate variability using relative RR intervals. Computing in Cardiology, 42, 609–612.

    Google Scholar 

  • Wang, J. S., Lin, C. W. and Yang, Y. T. C. (2013). A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition. Neurocomputing, 116, 136–143.

    Article  Google Scholar 

  • Xu, Q., Nwe, T. L. and Guan, C. (2014). Cluster-based analysis for personalized stress evaluation using physiological signals. IEEE J. Biomedical and Health Informatics 19,1, 275–281.

    Article  Google Scholar 

  • Yi, D., Su, J., Liu, C. and Chen, W. H. (2017). Personalized driver workload inference by learning from vehicle related measurements. IEEE Trans. Systems, Man, and Cybernetics: Systems 49,1, 159–168.

    Article  Google Scholar 

  • Zhang, Z., Zhou, Y., Chen, Z., Tian, X., Du, S. and Huang, R. (2013). Approximate entropy and support vector machines for electroencephalogram signal classification. Neural Regeneration Research 8,20, 1844–1852.

    Google Scholar 

  • Zhao, C., Zheng, C., Zhao, M., Tu, Y. and Liu, J. (2011). Multivariate autoregressive models and kernel learning algorithms for classifying driving mental fatigue based on electroencephalographic. Expert Systems with Applications 38,3, 1859–1865.

    Article  Google Scholar 

Download references

Acknowledgement

This work was partly supported by the Brain Korea 21 Plus Project (Center for Creative SOC Infrastructure System Technology, 21A20132000003) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning and Korea Ministry of Land, Infrastructure and Transport (MOLIT) as Smart City Master and Doctor Course Grant Program. The authors would like to thank Professor Wonjong Rhee of Seoul National University for his valuable comments, and the Korea Automotive Technology Institute (KATECH) for providing the Driver-Vehicle Interaction (DVI) dataset.

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, KAIST, Daejeon, 34141, Korea

    Yuna Noh, Seyun Kim & Yoonjin Yoon

  2. Maritime Transportation Big Data Office, Korea Maritime Transportation Safety Authority, Areumseo-gil 27, Sejong, 30100, Korea

    Yuna Noh

  3. Department of Industrial & Systems Engineering, KAIST, Daejeon, 34141, Korea

    Young Jae Jang

Authors
  1. Yuna Noh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seyun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Young Jae Jang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yoonjin Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yoonjin Yoon.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Noh, Y., Kim, S., Jang, Y.J. et al. Modeling Individual Differences in Driver Workload Inference Using Physiological Data. Int.J Automot. Technol. 22, 201–212 (2021). https://doi.org/10.1007/s12239-021-0020-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12239-021-0020-8

Key Words

Search

Navigation