Abstract
Continuous monitoring of users’ comfort level in automated driving could allow for optimizing human-automation teaming in this domain. Physiological parameters such as heart rate, eye blink frequency and pupil diameter are promising potential indicators for discomfort. In a driving simulator study, 20 participants experienced three automated close approach situations to a truck driving ahead and could report discomfort continuously by a handset control. Heart rate was measured by a smartband, and eye related parameters by eye tracking glasses. Two mathematical models, a logistic regression model and a Support Vector Machine (SVM) model, were compared for estimating discomfort by combing these physiological parameters. Both models showed similar prediction performance with slightly better prediction accuracy for the logistic model, even if the number of parameters (model complexity) contained in the logistic model was far less than in the SVM model.
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References
ERTRAC: Connected Automated Driving Roadmap. European Road Transport Research Advisory Council, European Road Transport Research Advisory Council (2019). https://www.ertrac.org/uploads/documentsearch/id57/ERTRAC-CAD-Roadmap-2019.pdf
Beggiato, M., Hartwich, F., Krems, J.: Physiological correlates of discomfort in automated driving. Transp. Res. Part F Traffic Psychol. Behav. 66, 445–458 (2019). https://doi.org/10.1016/j.trf.2019.09.018
Techer, F., Ojeda, L., Barat, D., Marteau, J.-Y., Rampillon, F., Feron, S., Dogan, E.: Anger and highly automated driving in urban areas: the role of time pressure. Transp. Res. Part F Traffic Psychol. Behav. 64, 353–360 (2019). https://doi.org/10.1016/j.trf.2019.05.016
Beggiato, M., Hartwich, F., Krems, J.: Using smartbands, pupillometry and body motion to detect discomfort in automated driving. Front. Hum. Neurosci. 12, 3138 (2018). https://doi.org/10.3389/fnhum.2018.00338
Beggiato, M., Rauh, N., Krems, J.: Facial expressions as indicator for discomfort in automated driving. In: Ahram, T., et al. (eds.) Intelligent Human Systems Integration, IHSI 2020. AISC 1131. Springer Nature Switzerland (2020). https://doi.org/10.1007/978-3-030-39512-4_142
Bishop, C.: Pattern Recognition and Machine Learning. Springer New York Inc. (2006). ISBN 0387310738
Acknowledgement
All authors acknowledge funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). Project-ID 416228727 – SFB 1410.
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Dommel, P., Pichler, A., Beggiato, M. (2021). Comparison of a Logistic and SVM Model to Detect Discomfort in Automated Driving. In: Russo, D., Ahram, T., Karwowski, W., Di Bucchianico, G., Taiar, R. (eds) Intelligent Human Systems Integration 2021. IHSI 2021. Advances in Intelligent Systems and Computing, vol 1322. Springer, Cham. https://doi.org/10.1007/978-3-030-68017-6_7
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DOI: https://doi.org/10.1007/978-3-030-68017-6_7
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