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

Area of Science:

• Automotive Engineering
• Human Factors Engineering
• Transportation Safety

Background:

• Motion sickness is a significant challenge for autonomous vehicle (AV) passenger comfort and acceptance.
• Understanding the relationship between vehicle dynamics and motion sickness is crucial for designing comfortable AVs.

Purpose of the Study:

• To quantify motion sickness thresholds for key vehicle dynamics parameters in AVs.
• To develop a predictive model for motion sickness based on these parameters.
• To validate the model and assess the effectiveness of dynamic motion control in reducing sickness.

Main Methods:

• Experiments were conducted on curved (S-shaped) and straight roads to measure passenger responses.
• Thresholds for lateral acceleration (Δa_y) and Z-axis angular velocity (Gyro_z_mean) were determined for moderate (M2) and severe (M3) motion sickness.
• Thresholds for maximum longitudinal velocity (v_x_max) were also identified for straight-road scenarios.
• A predictive model was constructed and validated using real-vehicle testing.

Main Results:

• On S-curves, M2/M3 thresholds for Δa_y were 0.398/0.419 m/s², and for Gyro_z_mean were 6.876/8.022 °/s.
• On straight roads, M2/M3 thresholds for Δa_y were 0.394/0.648 m/s², and for v_x_max were 13.961/18.492 m/s.
• The predictive model achieved 86% accuracy for M2 and 82% for M3.
• Dynamic control reduced motion sickness risk by 39.7%.

Conclusions:

• Specific thresholds for vehicle dynamics parameters inducing motion sickness were established for different road types.
• The developed predictive model accurately forecasts motion sickness levels.
• Dynamically managing vehicle motion within these thresholds is an effective strategy to enhance ride comfort and reduce motion sickness in autonomous vehicles.