Abstract
While driving autonomously, trust and acceptance are important human factors to consider. Detecting uncomfortable and stressful situations while driving could improve trust, driving quality and overall acceptance of autonomous vehicles through adaption of driving style and user interfaces. In this paper, we test a variety of sensors which could measure the stress of vehicle passengers in real-time. We propose a portable system that measures heart rate, skin conductance, sitting position, g-forces and subjective stress. Results show that correlations between self-reported, subjective stress and sensor values are significant and a neural network model can predict stress based on the measured sensor outputs. However, the subjective self-reported stress does not always match sensor evidence, which demonstrates the problem of subjectiveness and that finding one model that fits all test-subjects is a challenge.
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Notes
- 1.
GSR Sensor from Seeedstudio, http://wiki.seeedstudio.com/Grove-GSR_Sensor.
- 2.
HR Sensor from Seeedstudio, http://wiki.seeedstudio.com/Grove-Ear-clip_Heart_Rate_Sensor/.
- 3.
Seeedstudio Grove system, http://wiki.seeedstudio.com/Grove_System.
References
Niermann, D., Lüdtke, A.: Measuring driver discomfort in autonomous vehicles. In: International Conference on Intelligent Human Systems Integration, pp. 52–58. Springer, Cham (2020)
Storm, H., Myre, K., Rostrup, M., Stokland, O., Lien, M.D., Raeder, J.C.: Skin conductance correlates with perioperative stress. Acta Anaesthesiol. Scand. 46(7), 887–895 (2002)
Villarejo, M.V., Zapirain, B.G., Zorrilla, A.M.: A stress sensor based on galvanic skin response (GSR) controlled by ZigBee. Sensors 12(5), 6075–6101 (2012)
Minguillon, J., Perez, E., Lopez-Gordo, M., Pelayo, F., Sanchez-Carrion, M.: Portable system for real-time detection of stress level. Sensors 18(8), 2504 (2018)
Yamakoshi, T., Yamakoshi, K.I., Tanaka, S., Nogawa, M., Shibata, M., Sawada, Y., Rolfe, P., Hirose, Y.: A preliminary study on driver’s stress index using a new method based on differential skin temperature measurement. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 722–725 (2007)
Healey, J., Picard, R.W.: Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Syst. 6(2), 156–166 (2005)
Gao, H., Yüce, A., Thiran, J.P.: Detecting emotional stress from facial expressions for driving safety. In: IEEE International Conference on Image Processing (ICIP), pp. 5961–5965 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
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Niermann, D., Lüdtke, A. (2021). Predicting Vehicle Passenger Stress Based on Sensory Measurements. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1252. Springer, Cham. https://doi.org/10.1007/978-3-030-55190-2_23
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DOI: https://doi.org/10.1007/978-3-030-55190-2_23
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