Higher levels of automation in driving may allow drivers to engage in other activities, but may also increase the likelihood of Motion Sickness (MS). The exact causes of MS are not well understood, and various susceptibility factors(e.g. age, gender, ethnicity) can cause large individual differences. To better understand and predict MS, it is ideally studied in a safe and controlled environment, such as a driving simulator. However, the validity of driving simulator studies on MS as a proxy for on-road studies with real vehicles has not been properly evaluated. We conducted an experiment where the temporal aspects and symptom profiles of MS in a real-road driving scenario are compared to Simulator Sickness (SS) in a reproduction of this scenario in a motion-base driving simulator. A cohort of 25 participants was exposed to both the car and the simulator conditions. The scenarios consisted of sections of provocative(slaloming, stop-and-go) and normal driving. Sickening stimuli of the simulator were similar to the car accelerations in design (r = 0.51) but different in outcome (r = 0.27) as a result of motion cueing. MIsery Scale (MISC) scores on a 30 s interval, post-experiment Motion Sickness Assessment Questionnaire (MSAQ) scores, Galvanic Skin Response(GSR) and Electrogastrography (EGG) data were collected. Results showed significant correlations between the car and simulator conditions for 3 out of 4 MSAQ symptom categories (0.48 < r < 0.73, p < 0.02) and a relation (r = 0.57,p = 0.004) for individual sensitivity to sickness. Sickness onset times did not differ between the car and the simulator[F(1,308) = 4.80, p = 0.029], after Bonferroni corrections had been applied. Both MS and SS increased and decreased as a result of the driving style, with the effect being larger in the car condition, than in the simulator (for MISC [F(1,248)= 19.15, p = 0.000] and for GSR [F(1,230) = 5.55, p = 0.019]). Results from all four measures indicate that the severity of sickness was higher in the car as compared to in the simulator. EEG responses did not fully show expected outcomes. However, the signal quality was limited and dedicated EGG equipment may yield different results. Because individual sensitivity and temporal aspects of SS and MS were similar between the car and simulator but different in magnitude, we conclude relative validity for the simulator. As the human vestibular system, a prominent contributor in causing sickness, is solely sensitive to accelerations, we attribute the difference in magnitude due to downscaling of the vehicle motion in the simulator. In order to obtain absolute validity, either extensive training or considerable technological advances may be necessary.

Increased susceptibility to motion sickness, due to the transition away from driving, will be one of the major hurdles in the widespread use of automated vehicles. Sustained exposure to motion sickness can lead to the disuse of automated vehicle technology among users. Thus, there is a need to mitigate motion sickness. To do so, a robust model is desired which can predict motion sickness levels while also accounting for individual differences in susceptibility. Studies have been carried out to study the different dynamics of motion sickness and its development. However, the effect of motion amplitude has not yet been quantified.
This study investigated the amplitude dynamics of motion sickness. This was done by perturbing 17 participants along the fore-aft direction with acceleration amplitudes of 1, 1.5, 2 and 2.5 ms^-2 in four separate sessions. In the experiment, both subjective sickness scores and Galvanic Skin Response (GSR) was recorded. Using the subjective sickness scores, we explored variations of the Oman model of nausea that could capture the time-amplitude dynamics that were observed. Along with this, a neural network prediction model was proposed to predict motion sickness levels using GSR as a predictor, which could then be used as an objective measurement of motion sickness. Lastly, to better understand the MISC as a measure of motion sickness, we determined the functional mapping between subjective discomfort and the MISC scale.
Our findings show a monotonous increase in the rate of motion sickness development, but this increase is not linear, with a sudden change in slope after 1.5 ms^-2. This nonlinear increase was captured best by applying a power law to the input conflict vector instead of at the output as proposed in the original Oman model (Oman, 1990) Further, it is found that GSR can indeed be used as a predictor for motion sickness with an accuracy of 77% in the training, 66% in the validation and 62% in the holdout set. Finally, it is found that the subject discomfort has a power-law relation with MISC with a mean exponent of 1.28.
The developed prediction model as well as the variation of the Oman model can be used in other experiments to control the level of sickness to the desired trajectory. The developed models can enable adaptive control algorithms for path and motion planning to mitigate motion sickness. This is will in turn lead to a significant improvement in the experience in commuting with an automated vehicle.