Objective evaluation of prediction strategies for optimization-based motion cueing

Abstract

Optimization-based motion cueing algorithms based on model predictive control have been recently implemented to reproduce the motion of a car within the limited workspace of a driving simulator. These algorithms require a reference of the future vehicle motion to compute a prediction of the system response. Assumptions regarding the future reference signals must be made in order to develop effective prediction strategies. However, it remains unclear how the prediction of future vehicle dynamics influences the quality of the motion cueing. In this study two prediction strategies are considered. Oracle: the ideal prediction strategy that knows exactly what the future reference is going to be. Constant: a prediction strategy that ignores every future change and keeps the current vehicle’s linear accelerations and angular velocities constant. The two prediction strategies are used to reproduce a sequence of maneuvers between 0 and 50 km/h. A comparative analysis is carried out to objectively evaluate the influence of the prediction strategies on motion cueing quality. Dedicated indicators of correlation, delay and absolute error are used to compare the effects of the adopted prediction on simulator motion. Also the motion cueing mechanisms adopted by the different conditions are analyzed, together with the usage of simulator workspace. While the constant strategy provided reasonable cueing quality, the results show that knowledge of the future vehicle trajectory reduces the delay and improves correlation with the reference trajectory, it allows the combined usage of different motion cueing mechanisms and increases the usage of workspace.

High Speed Emulation in a Vehicle-in-the-Loop Driving Simulator

“…While it is common to conduct human subjects studies to validate novel driving simulator designs, objective criteria set clear standards for evaluating drivers' level of immersion that can be applied even if the simulator design changes. Underscoring this approach, recent work by Grottoli et al [26] and by Biemelt et al [27] use objective metrics of vestibular feedback quality and known perception thresholds to assess the effectiveness of different motion cueing algorithms. In our method, the vestibular feedback experienced by the driver is verified objectively by comparing the difference between the real and reference yaw rate to known yaw perception thresholds derived by Nesti et al from human subjects studies [21].…”

High Speed Emulation in a Vehicle-in-the-Loop Driving Simulator

“…While it is common to conduct human subjects studies to validate novel driving simulator designs, objective criteria set clear standards for evaluating drivers' level of immersion that can be applied even if the simulator design changes. Underscoring this approach, recent work by Grottoli et al [26] and by Biemelt et al [27] use objective metrics of vestibular feedback quality and known perception thresholds to assess the effectiveness of different motion cueing algorithms. In our method, the vestibular feedback experienced by the driver is verified objectively by comparing the difference between the real and reference yaw rate to known yaw perception thresholds derived by Nesti et al from human subjects studies [21].…”

MPC-Based Motion-Cueing Algorithm for a 6-DOF Driving Simulator with Actuator Constraints

“…Interquartile range (IQR) of the actuator length can be used to analyze how an MCA uses the available actuator workspace [38]. It is a measure of variability and is defined as the difference between the 75th and 25th percentile of the given sample.…”