In the near future, travelling in vehicles will no longer be in regular vehicles, but in automated vehicles. The share of automated vehicles is predicted to increase significantly within 20 years. Passengers in automated vehicles will engage in non-driving tasks, such as sleeping, reading, working or just otherwise spending time on their phone. This will result in motion sickness becoming more prevalent, as passengers will no longer pay attention to the road. Therefore, there is a need for research in motion sickness. To further our understanding of motion sickness and possible mitigation strategies, mathematical models of motion sickness need to be developed. The temporal dynamics of motion sickness can be captured in the so called 'Oman model' \cite{Oman90}. However, most literature use group averaged parameters and motion sickness incidence to describe motion sickness. These methods do not capture well enough how individuals respond to sickening stimuli, as recent studies showed that individuals have strongly varying responses to various frequencies. Only, estimating individual motion sickness parameters is costly thus far, requiring multiple experiments to estimate the parameters. This study explains an optimal experiment design, where the input is varied in real-time closed loop manner such that the information content in the input is maximized for estimation of parameters, rusulting in the fact that individual motion sickness parameters could be estimated in a single experiment. Results show that on average, within the first 63 minutes, most parameter estimations have converged. The resulting RMSE is 1.06 on the MISC scale, comparing to other literature. This shows that the frequency and temporal dynamics of motion sickness and an individual level can be estimated at a drastically faster rate than previous methods. To our knowledge this is the first use of optimal experiment design techniques to asses the dynamics of human responses to stimuli in general, which is an important milestone for cybernetics research.

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.

Following the literature review, our goal was to study the effect and interaction of motion sickness and motivation on cognitive performance in a reading comprehension task and the associated workload with the task. We chose UCKAT reading tasks for our cognitive task, monetary incentive and ranks as our motivator and a multisine sickening motion profile on a simulator as
our motion variable. We exposed participants to 4 conditions, employing a within-subject experiment design, manipulating our independent variables motion and motivation. We collected motion sickness data via the motion sickness susceptibility questionnaire, misery scale and motion
sickness assessment questionnaire; motivation data via the situational motivation scale; workload data via the NASA TLX workload scale and task performance data via the total score obtained, the total time spent on the task and the average time spent per question. We found that our motion profile caused motion sickness in participants, with some evidence for habituation. We also found some evidence for training effects present in our data. Performance decrements, associated workload and motivation scores across the 4 conditions were statistically similar and we could not conclusively prove our hypotheses. Further analysis showed that amotivation scores almost showed significant effect on task performance which does match anecdotal evidence. MSAQ scores also negatively affected how much time people could spend on a cognitive task. We found that workload scores of participants increased significantly with increase in motion sickness which could give
us an insight on performing cognitive tasks under sickness. Overall, our experiment design could not show the trends that we had hypothesized, and we obtained partial results via our secondary analysis. Our findings indicate that further attention is to be given to the motivation variable to make it more robust. Further, a much large sample size is needed to better test our hypotheses, with perhaps, a mixed subject design for our study. Our study also showed an unexpected interaction of lateral and londitudinal motion profiles, causing significantly higher levels of sickness than what was predicted using existing models, which warrants further research into the same.