Enhancing Passenger Comfort via Real-Time Motion Sickness Mitigation in a Vehicle Simulator

This paper validates a real-time system for mitigating motion sickness using readily accessible vehicle CAN-bus data and an interpretable CatBoost model. This work makes two primary contributions from a study in a vehicle simulator. The first is a framework for generating and delivering interpretable driving recommendations. The model’s output is used to identify the specific root causes of discomfort and translate them into clear, actionable advice, with the most effective communication method established through a comprehensive Human-Machine Interface (HMI) study. The second contribution is a semi-autonomous, closed-loop control system that performs direct interventions to enhance comfort. Crucially, this system uses the very same recommendations as a transparency layer for its automated actions; when a correction is made by the vehicle, the interpretable reason for the intervention is simultaneously communicated to the driver. The system’s efficacy is demonstrated in a comparative study, wherein a substantial reduction in high and moderate motion sickness scenarios and a significant increase in comfortable driving time are observed. Through this research, a practical framework is validated in which automated interventions are rendered transparent, proving that enhanced comfort can be achieved in synergy with driver understanding.