Research on a motion sickness prediction model for vehicle occupants based on vehicle dynamics parameter thresholds.

The ride comfort of autonomous vehicles is affected by motion sickness. This study quantifies the thresholds of vehicle dynamics parameters that induce motion sickness in both curved and straight-road scenarios, and constructs a predictive model. The results show that on S-shaped curves, the lateral acceleration thresholds (Δay) for moderate motion sickness (M2) and severe motion sickness (M3) are 0.398 m/s2 and 0.419 m/s2, respectively, while the Z-axis angular velocity thresholds (Gyroz_mean) are 6.876°/s and 8.022°/s. In straight-road scenarios, the Δay thresholds for M2 and M3 are 0.394 m/s2 and 0.648 m/s2, respectively, and the maximum longitudinal velocity (vx_max) reaches 13.961 m/s and 18.492 m/s. The proposed model achieves an accuracy of 86% for M2 and 82% for M3. Real-vehicle validation demonstrated that dynamically controlling vehicle motion states to maintain lateral acceleration, angular velocity, and longitudinal velocity below the specified thresholds reduced overall motion sickness risk by 39.7%.