This study investigates the effectiveness of vehicle dynamics and road environment data for predicting lateral accelerations and yaw rates. This prediction is used to preposition a simulator in anticipation of this motion. A prediction model was created using the road curvature at a specific look-ahead
time and the current vehicle velocity. The model was used to estimate future lateral accelerations and yaw rates for a predefined trajectory on a rural road. The proposed method was used in a human-in-the-loop experiment with 36 participants on a small hexapod. The lateral tilt and yaw gain could be doubled using prepositioning, increased the objective cueing quality, measured in cross-coefficient correlation and absolute difference. The subjective cueing quality was measured through perceived motion incongruence at different road sections and a posthoc questionnaire using a Likert scale. While higher subjective ratings were observed for conditions using lateral prepositioning, no statistically significant results were found. However, workspace management was improved using the prediction and prepositioning model. This allowed for less conservative tuning parameters, significantly improving the objective cueing quality. This method can increase cueing capabilities of a simulator and can be implemented in other driving scenarios, without the need for training data or large computation times.

A necessity in driving simulation testing is to understand and attenuate simulator sickness in urban environments to reduce the number of undesired drop-outs. This final thesis explains a 6 degree-of-freedom simulator sickness prediction model based on observer theory including the visual system. The model incorporates state-of-the-art knowledge of human spatial orientation perception and qualitative theories that try to explain motion sickness. Predictions are made regarding the simulator sickness incidence and expected rotary and translational motions for different motion and sensory paradigms. A between-subjects experiment was conducted to verify the model. Furthermore, the effect of adding scaled, but veridical, yaw motion to a simulator on the sickness incidence and severity while driving in urban environments was investigated. Participants were required to indicate a misery scale score every minute and to fill in the simulator sickness questionnaire prior to and after the experiment. Additionally, the perceived motion incongruence was measured together with head movements. Three cases were considered; a no-motion case, a case with a scaled yaw movement and a case with a scaled yaw movement together with pitch and roll motions. A significant relationship has been found between the simulator sickness prediction model outputs and the experimental outputs. Therefore, the model with the implemented visual system could be used to better understand
and predict simulator sickness. A significant relationship has also been found between the measured perceived motion incongruence and the simulator sickness incidence. This perceived motion incongruence can be used to quantify a part of the qualitative sensory rearrangement theory. Furthermore, humans that indicated lower sickness scores, moved their head significantly more along with the direction of the curve. Head movement strategies can be taught to participants of simulator studies to attenuate simulator sickness symptoms. Finally, significantly less participants dropped out in the cases that included simulator motion when driving in urban environments. The findings in this final thesis could assist in attenuating the number of drop-outs during driving simulation testing in urban scenarios.