Optimizing an Optimization-Based MCA using Perceived Motion Incongruence Models

Optimizing an Optimization-Based MCA using Perceived Motion Incongruence Models

Cleij, D. (TU Delft Control & Simulation; Max Planck Institute for Biological Cybernetics)
Pool, D.M. (TU Delft Control & Simulation)
Mulder, Max (TU Delft Control & Simulation)
Bülthoff, Heinrich H. (Max Planck Institute for Biological Cybernetics)

2020

In this paper the potential of Motion Incongruence Rating (MIR) models for the optimization of Motion Cueing Algorithms (MCAs) is investigated. In a human-in-the-loop simulator experiment, two optimization-based MCAs are compared for a roundabout scenario simulated on a medium-stroke hexapod simulator. The first MCA uses standard cueing error weights from reference literature in its cost function, while for the second case these weights were based on a MIR model fitted to previous experiment data. Results show that such models provide a promising cueing error weight estimation method for optimization-based MCAs, but also highlight the limitations of these models due to, for example, their dependency on the richness of the datasets to which they are fitted.

Motion cueing algorithms
motion incongruence models
model predictive control
continuous ratings

http://resolver.tudelft.nl/uuid:0c34b1dd-2e36-4625-b767-a8c5063128d9

53-60

Driving Simulation Conference Europe 2020 VR, 2020-09-09 → 2020-09-11, Antibes, France

Institutional Repository

conference paper

© 2020 D. Cleij, D.M. Pool, Max Mulder, Heinrich H. Bülthoff