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.