Motion Sickness Prediction Based on Passenger's Self Evaluation

Passengers performing non-driving related tasks in a self-driving vehicle, e.g., working on a laptop, are at high risk of developing symptoms of motion sickness. In the future, intelligent vehicles will use their own strategies and new technologies to prevent motion sickness and therefore enable the benefit of autonomous driving for the passengers. The current methods of objective assessment, modeling, prediction, and prevention of motion sickness show good performance applied to a group of passengers, but have difficulties when applied on an individual level. Therefore, pasenger's subjective self-assessment remains difficult to replace as a reference in motion sickness research and will likely be required in future intelligent vehicles. We demonstrate that the susceptibility, intensity, and onset of motion sickness can be predicted based on passenger's self-assessment using data collected in three real-world driving experiments. This way, we show that the concept of classifying motion sickness based on questionnaire results can be successfully applied, not only in virtual reality scenarios, but also in car sickness situations. As a result, future intelligent vehicles could adapt model parameters or choose avoidance strategies for respective passengers, based on classification algorithms using information derived from a questionnaire.