Modelling the relationship between the objective measures of car sickness

Car sickness is anticipated to occur more frequently in self-driving vehicles because of their design, especially the electronics and seating arrangements optimized for work and entertainment. Therefore, mitigating motion sickness is a crucial research area that is essential to the effective use of electronics in autonomous vehicles and, ultimately, their broad adoption. An investigation using machine learning techniques in combination with physiological measures (electrocardiogram, electrodermal activity and head movement) was done to detect and predict the severity of car sickness. A total of 40 adults (20 male and 20 female) were exposed to two 20-minute rides on a motion-base simulator, one while reading and one while performing no task. Car sickness incidence and severity were subjectively measured during the conditions using the Fast Motion Sickness Scale (FMS) questionnaire every two minutes and the Simulator Sickness Questionnaire (SSQ) at the beginning, midpoint and end of the experiment. Car sickness symptoms were successfully elicited in 31 participants (77.5%) while avoiding simulator sickness. Head movement had the strongest relationship with car sickness, and there was a moderate correlation between heart rate and skin conductance, and with a subset of participants, heart rate had a moderate correlation with car sickness. A classification score of 77% distinguishing between motion-sick and non-motion-sick participants was found using the random forest model. Overall, the findings suggest that physiological measures alone cannot be relied upon to reliably detect or predict the onset or severity of car sickness in real-time.