Probabilistic Maneuver Prediction for Motion Cueing in Driving Simulation

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Potential usage of the workspace of driving simulators is restricted by a lack of knowledge on oncoming maneuvers, due to the unpredictable nature of human drivers. This research aims to explore the possibility of using information from driver inputs, dynamic vehicle states, and features of the road environment to improve motion cueing through simulator prepositioning. Probabilistic models were established to predict longitudinal maneuvering for a short scenario consisting of a drive on a two-lane road through a rural area. By combining the accelerator deflection, the vehicle’s velocity and the future speed limit in a logistic regression model, the area under the receiver operating characteristic curve was 0.84 for acceleration prediction and 0.77 for deceleration prediction, using a lookahead time of 5 seconds. In order to use these predictions for motion cueing, the prediction model was extended to a prepositioning module. The proposed design was tested in combination with a classical washout algorithm on a small hexapod-based driving simulator in a human-in-the-loop experiment. No distinguishable results were obtained in objective and subjective evaluation of the motion cueing quality, as possibilities to improve motion cueing were limited with the experimental setup used. Nevertheless, the workspace usage was improved significantly. The average distance that could be maintained from the edge of the simulator’s workspace, could be increased from 0.025 meter to 0.084 meter, for equivalent motion cueing.