Introduction With the increasing prevalence of electric vehicles, motion sickness has emerged as a critical factor impairing passenger comfort. Current studies relying on simulated driving face limitations in replicating real-road conditions. Methods We conducted real-vehicle experiments across six roadway scenarios: one-way left turn (R1), linear acceleration/deceleration (R2), sudden arrest-activation (R3), uphill S-curve (R4), downhill S-curve (R5), and one-way right turn (R6). A synchronized system (BioRadio + vehicle gyroscopes) captured subjective ratings from participants ( n = 10) and objective data. Results Significant changes occurred in mean values of GSRmean , HRmean , RMSSD, and RESPmean during motion sickness ( p 0.05), while standard deviations ( GSRSD , RESPSD ) showed no significance. Motion sickness severity ranked as: R4 (8.4) R5 (7.7) R3 (6.3) R2 (4.4) R1 (2.0) R6 (1.4), confirming S-curves as the primary trigger. Discussion The logistic regression model achieved 81.25% accuracy in predicting motion sickness states. This study provides empirical evidence for optimizing vehicle motion control and road design to enhance passenger comfort.
Tang et al. (Fri,) studied this question.