Perception-Based Motion Cueing: A Cybernetics Approach to Motion Simulation

Abstract

The goal of vehicle motion simulation is the realistic reproduction of the perception a human observer would have inside the moving vehicle by providing realistic motion cues inside a motion simulator. Motion cueing algorithms play a central role in this process by converting the desired vehicle motion into simulator input commands with maximal perceptual fidelity, while remaining within the limited workspace of the motion simulator. By understanding how the one’s own body motion through the environment is transduced into neural information by the visual, vestibular and somatosensory systems and how this information is processed in order to create a whole percept of self-motion we can qualify the perceptual fidelity of the simulation. In this chapter, we address how a deep understanding of the functional principles underlying self-motion perception can be exploited to develop new motion cueing algorithms and, in turn, how motion simulation can increase our understanding of the brain’s perceptual processes. We propose a perception-based motion cueing algorithm that relies on knowledge about human self-motion perception and uses it to calculate the vehicle motion percept, i.e. how the motion of a vehicle is perceived by a human observer. The calculation is possible through the use of a self-motion perception model, which simulate the brain’s motion perception processes. The goal of the perception-based algorithm is then to reproduce the simulator motion that minimizes the difference between the vehicle’s desired percept and the actual simulator percept, i.e. the “perceptual error”. Finally, we describe the first experimental validation of the new motion cueing algorithm and shown that an improvement in the current standards of motion cueing is possible

Continuous Subjective Rating of Perceived Motion Incongruence During Driving Simulation

“…The perceptual system model PS can be used to minimize the motion mismatches, by implementing it as a cost function in the optimization algorithms for MCAs. The weight parameters of the simple model described here PS could for example be used to replace the tuned weights in cost functions for MCA optimization based on adaptive [34] or model predictive control [12] algorithms.…”

“…In both the cases, the only time-varying information of MCA quality was obtained via questionnaires on specific parts of the simulation. In [12], an offline rating (OR) method based on magnitude estimation with cross-modality matching was developed and used to detect differences between MCAs during car motion simulation. The MCA rating results obtained in these studies are time invariant and thus cannot be easily correlated with the timevarying motion mismatches.…”