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Abstract:
This paper describes a perception-based motion cueing (PBMC) algorithm, which aims to bridge the gap between what is known about human self-motion perception and what is currently used in motion simulation. In PBMC, motion perception knowledge is explicitly incorporated by means of a perception model and a cost function. PBMC has the potential of improving the realism of the motion simulation by exploiting the limitations and ambiguities of human self-motion perception and increasing the utilization of the simulator envelope, while reducing the need for parameter tuning. The PBMC algorithm was compared to a classical filter-based approach in an experimental study. To allow for a robust and reliable comparison, an evaluation method for motion cueing algorithms (MCAs) based on psychophysical techniques was developed. Results show that the PBMC approach received significantly higher ratings than the filter-based approach. This demonstrates the potential of the PBMC approach to improve motion cueing in vehicle simulation.
