Investigating the impact of different road scenarios on the induction intensity of motion sickness in electric vehicle passengers

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.