Motion-Cuing Algorithms

Objective: The aim of this study was to characterize the human response to motion-cuing algorithms (MCAs) by comparing users’ perception to several proposed objective indicators. Background: Other researchers have proposed several MCAs, but few improvements have been achieved lately. One of the reasons for this lack of progress is that fair comparisons between different algorithms are hard to achieve, for their evaluation needs to be performed with humans and the tuning process is slow. Method: This characterization is performed by means of a comparison of the subjective perception of vehicle simulation users (90 participants) against several proposed objective indicators that try to measure MCA performance. Two motion platforms (3 and 6 degrees of freedom [DoF]) and two vehicle simulators (a driving simulator and a speedboat simulator) were tested using the classical washout algorithm, considered to be the main reference in MCA literature. Results: Results show that users are more sensitive to correlation and delay with respect to the expected motion rather than its magnitude and that specific force is more of a factor than angular speed in the driving simulator. The opposite happens in the speedboat simulator. Conclusions: Human drivers’ reaction to MCA is mainly characterized by the normalized Pearson correlation between output and input signals of the algorithm. This finding validates the main MCA strategy that consists of downscaling the signals and slightly distorting their frequency spectrum. The 6-DoF simulator is perceived as a modest improvement of the 3-DoF platform. Applications: These results set the basis for future automatic tuning, evaluation, and comparison of MCA in motion platforms.

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