Authors:
Felix Ellensohn, Joost Venrooij, Markus Schwienbacher, Daniel Rixen
Keywords:
motion cueing algorithms, optimization-based, continuous rating
Abstract:
This paper describes a global optimization scheme that is employed as a motion cueing algorithm (MCA) for a 9 degrees of freedom (DoF) driving simulator. The paper describes the evaluation of the MCA in an experiment with 35 participants. Herein, the MCA is compared to a commercial, state-of-the-art, optimization-based MCA with the goal to illustrate the potential improvements in motion cueing quality that the novel MCA could provide. The experiment design includes a continuous rating method, where the participants continuously evaluate deviations between expected and perceived motions. Rating results confirm the potential of this novel MCA approach. Furthermore, the ratings are used to train a linear rating model. Here, the objective deviations between the physical motions of the simulator and the simulated vehicle functions as input. The model approximates the human perceptual system and the human rating procedure to produce a modelled rating. The proposed model achieves high correlations to the reference rating with the training set. However, it is questionable whether the linear model can be applied generally as the model does not accurately capture the ratings in a testing-set.
Ellensohn F.; Venrooij J.; Schwienbacher M. and Rixen D. Experimental evaluation of an optimization-based motion cueing algorithm In: Proceedings of the Driving Simulation Conference 2018 Europe VR, Driving Simulation Association, Antibes, France, 2018, pp. 61-62
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@inproceedings{Ellensohn2018,
title = {Experimental evaluation of an optimization-based motion cueing algorithm},
author = {Felix Ellensohn and Joost Venrooij and Markus Schwienbacher and Daniel Rixen},
editor = {Andras Kemeny and Florent Colombet and Frédéric Merienne and Stéphane Espié},
isbn = {978-2-85782-734-4},
year = {2018},
date = {2018-09-05},
booktitle = {Proceedings of the Driving Simulation Conference 2018 Europe VR},
pages = {61-62},
address = {Antibes, France},
organization = {Driving Simulation Association},
abstract = {This paper describes a global optimization scheme that is employed as a motion cueing algorithm (MCA) for a 9 degrees of freedom (DoF) driving simulator. The paper describes the evaluation of the MCA in an experiment with 35 participants. Herein, the MCA is compared to a commercial, state-of-the-art, optimization-based MCA with the goal to illustrate the potential improvements in motion cueing quality that the novel MCA could provide. The experiment design includes a continuous rating method, where the participants continuously evaluate deviations between expected and perceived motions. Rating results confirm the potential of this novel MCA approach. Furthermore, the ratings are used to train a linear rating model. Here, the objective deviations between the physical motions of the simulator and the simulated vehicle functions as input. The model approximates the human perceptual system and the human rating procedure to produce a modelled rating. The proposed model achieves high correlations to the reference rating with the training set. However, it is questionable whether the linear model can be applied generally as the model does not accurately capture the ratings in a testing-set.},
keywords = {continuous rating, motion cueing algorithms, optimization-based},
}
Download .bib file
TY - CONF
TI - Experimental evaluation of an optimization-based motion cueing algorithm
AU - Ellensohn, Felix
AU - Venrooij, Joost
AU - Schwienbacher, Markus
AU - Rixen, Daniel
C1 - Antibes, France
C3 - Proceedings of the Driving Simulation Conference 2018 Europe VR
DA - 2018/09/05
PY - 2018
SP - 61
EP - 62
LA - en-US
PB - Driving Simulation Association
SN - 978-2-85782-734-4
L2 - https://proceedings.driving-simulation.org/proceeding/dsc-2018/experimental-evaluation-of-an-optimization-based-motion-cueing-algorithm
ER -
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