ORIGINAL RESEARCH article
Front. Psychol.
Sec. Perception Science
Volume 16 - 2025 | doi: 10.3389/fpsyg.2025.1615498
Investigating the Impact of Different Road Scenarios on the Induction Intensity of Motion Sickness in Electric Vehicle Passengers
Provisionally accepted
- 1Chongqing University of Arts and Sciences, Chongqing, China
- 2China Automotive Engineering Research Institute Co., Ltd, Chongqing, China
- 3School of Vehicle and Mobility, Tsinghua University, Beijing, Beijing, China
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With the increasing prevalence of electric vehicles, motion sickness has gained growing attention as a significant factor affecting passenger comfort. However, current studies on motion sickness often rely on simulated driving, which may not accurately replicate real road conditions. Therefore, this research investigates the impact of different roadway scenarios on the severity of motion sickness using real vehicle experiments. The test conditions included one way left turn (R1), Linear acceleration and deceleration (R2), sudden arrest-activation(R3), uphill S-curve (R4), downhill S-curve (R5), and one way right turn (R6). We developed a synchronized data collection system using BioRadio and vehicle gyroscopes to capture subjective motion sickness ratings from participants (n=10) alongside objective data, including skin conductance (GSR), heart rate (HR), and respiratory rate (RESP). The analysis indicates that the mean values of mean GSR , mean HR ,RMSSD, and mean RESP exhibited significant changes during motion sickness, while the standard deviations of SD GSR and SD RESP showed no significant differences. The severity of motion sickness induced by the six typical roadway scenarios was ranked as follows: R4 (8.4) > R5 (7.7) > R3 (6.3) > R2 (4.4) > R1 (2.0) > R6 (1.4), demonstrating that S-bend scenarios are the most likely to provoke discomfort from motion sickness. Furthermore, the logistic regression model we established achieved an accuracy of 81.25% in predicting motion sickness states. This study has important implications for enhancing passenger comfort during travel.
Keywords: motion sickness severity, physiological signals, real-vehicle dynamics measurements, Road characteristics, predictive model
Received: 22 Apr 2025; Accepted: 07 Jul 2025.
Copyright: © 2025 Tang, Luo, Ding, Tang and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Bangbei Tang, Chongqing University of Arts and Sciences, Chongqing, China
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