Objective comparison of motion cueing algorithms for driving simulator

Abstract

Inertial or motion cue plays a significant role for the achievement of an immersive feeling in driving simulators. The control loop is responsible for mimicking as close as possible the real vehicle accelerations as the inertial feedback to the driver, while keeping the motion system within its physical limitations. Several algorithms have been designed for this purpose, seeking for the optimal compromise between realistic accelerations fidelity and actuation dynamics restrictions. Motion cueing algorithms can hence vary depending on the dynamics of the maneuver, computational efficiency required and motion system configuration. Several algorithms can be found in literature, from the classical approach, based on a combination of high and low-pass filters on the vehicle accelerations, to more recent strategies based on Model Predictive Control (MPC). Assessing the success of one solution is a complex task that would involve the prototype implementation on hardware as well as the availability of several test subjects. Nevertheless the result would be a subjective evaluation of the performance. This study proposes instead a preliminary analysis of the performance of motion cueing algorithms with objective evaluation by means of a human motion perception model.