Diane Cleij

PhD student

Supervisors: and

use of motion-based simulators is made for a wide variety of vehicles. One of the main challenges of a motion-based simulator is to cope with its limited workspace. Motion Cueing Algorithms (MCA) have been developed to map the vehicle inertial motions onto the simulator motion space, while minimizing the mismatch between the visual and inertial motion cues. However, a mismatch always remains.

In my research I aim to investigate the relation between these mismatches and the time varying perceived realism of the motion simulation.

Background

In 2011 I graduated from the Technical University in Delft, The Netherlands, in the field of control and simulation at the faculty of Aerospace Engineering. During my internship I did research on differences between at Entropy Inc, San Diego, USA. For my master thesis I developed a haptic shared controller based on different states of the human neuromuscular system and analyzed the .

After my master thesis I worked in industry for two years as a mechatronics designer at Alten Mechatronics and ASML. In 2014 I started my PhD here at the Max Planck Institute for Biological Cybernetics.

Selected publications:

• Continuous Subjective Rating of Perceived Motion Incongruence during Driving Simulation IEEE Transactions On Human-Machine Systems (2017)
• Comparison between filter- and optimization-based motion cueing algorithms for driving simulation Transportation Research Part F: Traffic Psychology and Behaviour (2017)
• Perception-based motion cueing: validation in driving simulation, DSC 2015 Europe: Driving Simulation Conference & Exhibition, Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2015)

PhD: Prediction of perceived motion incongruence between visual/inertial motion cues in a motion-based simulator

Introduction

To cope with the limited workspace of motion based simulators, motion cueing algorithms (MCA) are used to map the desired motions onto the simulator workspace. Because of this necessary mapping, mismatches between visual and inertial cues will always occur. Certain, but not all, of these mismatches can cause the subject to perceive an incongruence between the visual and inertial motions. This perceived motion incongruence (PMI) in turn, causes a decrease in simulation realism or can even result in simulator sickness.

To minimize this PMI, experts are often asked to tune the MCA. A more efficient way of improving an MCA is to use mathematical optimization algorithms. These algorithms, however, require a mathematical representation of the relation between PMI and the mismatches between visual and inertial motion cues. Currently such a representation does not yet exist.

Goals

• Develop a method to measure time-varying perceived motion incongruence between visual/inertial motion cues in motion simulation (PMI)
• Derive a model to predict this time-varying perceived motion incongruence
• Improve motion cueing algorithms using the derived model

Methods

The project consists of three main steps. In the first step a continuous measurement method will be developed to measure how the PMI in a motion-based simulator evolves over time. The second step is to use this measurement method to derive a model that predicts the PMI given the time signals of the visual and vestibular motion cues (e.g. linear acceleration and rotational velocity). To investigate the time-varying aspect of the PMI, we will first derive a PMI prediction model for simple, one degree of freedom, motions. This model will be used to derive a PMI prediction model for more complex, multiple degrees of freedom, motions. In the third and final step the PMI prediction model will be used to optimize MCAs.

Initial results

A measurement method for time-varying PMI, based on continuous rating, was developped and tested. The results for continuos rating during a simple and more ecological driving simulation are described in 1) and respectively.

The process of designing a PMI model using continuous rating of PMI is also described in 1) and an initial PMI model is presented. Currently, work is being done on a more elaborate PMI prediction model that also includes detection of different cueing error types such as scaled, missing and false cues. This cueing error detection algorithm allows for different weighing of these error types within the PMI model and improves the prediction power of the model significantly.

1) Cleij D, Venrooij J, Pretto P, Pool DM, Mulder M, Buelthoff HH, “Continuous Subjective Rating of Perceived Motion Incongruence during Driving Simulation”, IEEE Transactions on Human-Machine Systems, 2017 (In Press)

2) Cleij D, Venrooij J, Pretto P, Katliar M, Bülthoff HH, Steffen D, Hoffmeyer FW, Schöner H-P , “Comparison between filter- and optimization-based motion cueing algorithms for driving simulation”, Transportation Research Part F: Psychology and Behaviour, 2017 (In Press)