Abstract
Conditionally automated cars share the driving task with the driver. When the control switches from one to another, accidents can occur, especially when the car emits a takeover request (TOR) to warn the driver that they must take the control back immediately. The driver’s physiological state prior to the TOR may impact takeover performance and as such was extensively studied experimentally. However, little was done about using Machine Learning (ML) to cluster natural states of the driver. In this study, four unsupervised ML algorithms were trained and optimized using a dataset collected in a driving simulator. Their performances for generating clusters of physiological states prior to takeover were compared. Some algorithms provide interesting insights regarding the number of clusters, but most of the results were not statistically significant. As such, we advise researchers to focus on supervised ML using ground truth labels after experimental manipulation of drivers’ states.
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References
Merat, N., Jamson, A.H., Lai, F., Carsten, O.: Highly automated driving, secondary task performance, and driver state. Hum. Factors J. Hum. Factors Ergon. Soc. 54, 762–771 (2012)
Zeeb, K., Buchner, A., Schrauf, M.: What determines the take-over time? An integrated model approach of driver take-over after automated driving. Accid. Anal. Prev. 78, 212–221 (2015)
Johannes, B., Gaillard, A.W.: A methodology to compensate for individual differences in psychophysiological assessment. Biol. Psychol. 96, 77–85 (2014). https://doi.org/10.1016/j.biopsycho.2013.11.004
Giraud, C.: Introduction to High-Dimensional Statistics (2014). https://doi.org/10.1201/b17895
Darzi, A., Gaweesh, S.M., Ahmed, M.M., Novak, D.: Identifying the causes of drivers’ hazardous states using driver characteristics, vehicle kinematics, and physiological measurements. Front. Neurosci. 12 (2018). https://doi.org/10.3389/fnins.2018.00568
Meteier, Q., et al.: Classification of drivers’ workload using physiological signals in conditional automation. Front. Psychol. 12, 596038 (2021). https://doi.org/10.3389/fpsyg.2021.596038
Healey, J., Picard, R.W.: Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Syst. 6, 156–166 (2005)
Chen, L., Zhao, Y., Ye, P., Zhang, J., Zou, J.: Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers. Expert Syst. Appl. 85, 279–291 (2017)
Awais, M., Badruddin, N., Drieberg, M.: A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability. Sensors 17, 1991 (2017)
Li, N., Misu, T., Miranda, A.: Driver behavior event detection for manual annotation by clustering of the driver physiological signals. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 2583–2588 (2016)
Noh, Y., Kim, S., Jang, Y.J., Yoon, Y.: Modeling individual differences in driver workload inference using physiological data. Int. J. Automot. Technol. 22(1), 201–212 (2021). https://doi.org/10.1007/s12239-021-0020-8
Makowski, D., et al.: NeuroKit2: A Python Toolbox for Neurophysiological Signal Processing (2020). https://github.com/neuropsychology/NeuroKit. Accessed 28 Mar 2020
Acknowledgments
This work is part of the AdVitam project funded by the Hasler Foundation. We would also like to thank our colleagues who helped us during this project.
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de Salis, E. et al. (2022). Clustering of Drivers’ State Before Takeover Situations Based on Physiological Features Using Unsupervised Machine Learning. In: Ahram, T., Taiar, R. (eds) Human Interaction, Emerging Technologies and Future Systems V. IHIET 2021. Lecture Notes in Networks and Systems, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-030-85540-6_69
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DOI: https://doi.org/10.1007/978-3-030-85540-6_69
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