Authors:
Joost R. van der Ploeg, Diane Cleij, Daan M. Pool, Max Mulder, Heinrich H. Bülthoff
Keywords:
motion cueing, driving simulators, curve driving, model predictive control, continuous subjective ratings
Abstract:
Despite gaining popularity, the use of Motion Cueing Algorithms (MCAs) based on Model Predictive Control (MPC) remains challenging due to the required tuning of a large number of cost function parameters. This paper investigates the effects of two critical MPC cost function parameters, the lateral specific force and roll rate error weights (Way and Wp), on the motion cueing quality achieved with an MPC-based MCA for a curve driving scenario. An offline sensitivity analysis, which quantified the effects of varying Way and Wp on the Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC) of the resulting simulator motion outputs, shows that for the same percentage-wise variation, Way has a more pronounced effect on both cueing quality predictors than Wp. In addition, for both RMSE and PCC, the effects of Way and Wp are also found to be largely independent, i.e., without interaction effects. This was further tested in a passive human-in-the-loop experiment with 20 participants and with nine different Way and Wp parameter combinations as test conditions, performed in the hexapod moving-base simulator of the Max Planck Institute for Biological Cybernetics in Tubingen. The collected continuous ¨ rating data, which were found to be reliable for 18/20 participants, show a statistically significant variation across all experiment conditions, and especially a strong interaction effect of Way and Wp. Somewhat surprisingly, the overall lowest continuous ratings were given to the combination of both reference weight settings from earlier research (our baseline condition). In line with the interaction effect in the continuous data, an extended post-experiment correlation analysis shows that a weighted combination of lateral specific force RMSE and and roll rate RMSE above the roll rate perception threshold strongly correlates (ρ = 0.98) with the variation in mean continuous ratings across all experiment conditions. This approach can potentially be used for straightforward prediction of perceived motion cueing quality and offline MCA optimization.
Ploeg J.R.V.D.; Cleij D.; Pool D.M.; Mulder M. and Bülthoff H.H. Sensitivity Analysis of an MPC-based Motion Cueing Algorithm for a Curve Driving Scenario In: Proceedings of the Driving Simulation Conference 2020 Europe VR, Driving Simulation Association, Antibes, France, 2020, pp. 37-44
Download .txt file
@inproceedings{VanDerPloeg2020,
title = {Sensitivity Analysis of an MPC-based Motion Cueing Algorithm for a Curve Driving Scenario},
author = {Joost R. Van Der Ploeg and Diane Cleij and Daan M. Pool and Max Mulder and Heinrich H. Bülthoff},
editor = {Andras Kemeny and Jean-Rémy Chardonnet and Florent Colombet},
year = {2020},
date = {2020-09-09},
booktitle = {Proceedings of the Driving Simulation Conference 2020 Europe VR},
pages = {37-44},
address = {Antibes, France},
organization = {Driving Simulation Association},
abstract = {Despite gaining popularity, the use of Motion Cueing Algorithms (MCAs) based on Model Predictive Control (MPC) remains challenging due to the required tuning of a large number of cost function parameters. This paper investigates the effects of two critical MPC cost function parameters, the lateral specific force and roll rate error weights (Way and Wp), on the motion cueing quality achieved with an MPC based MCA for a curve driving scenario. An offline sensitivity analysis, which quantified the effects of varying Way and Wp on the Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC) of the resulting simulator motion outputs, shows that for the same percentage-wise variation, Way has a more pronounced effect on both cueing quality predictors than Wp. In addition, for both RMSE and PCC, the effects of Way and Wp are also found to be largely independent, i.e., without interaction effects. This was further tested in a passive human-in-the-loop experiment with 20 participants and with nine different Way and Wp parameter combinations as test conditions, performed in the hexapod moving-base simulator of the Max Planck Institute for Biological Cybernetics in Tubingen. The collected continuous ¨ rating data, which were found to be reliable for 18/20 participants, show a statistically significant variation across all experiment conditions, and especially a strong interaction effect of Way and Wp. Somewhat surprisingly, the overall lowest continuous ratings were given to the combination of both reference weight settings from earlier research (our baseline condition). In line with the interaction effect in the continuous data, an extended post-experiment correlation analysis shows that a weighted combination of lateral specific force RMSE and and roll rate RMSE above the roll rate perception threshold strongly correlates (ρ = 0.98) with the variation in mean continuous ratings across all experiment conditions. This approach can potentially be used for straightforward prediction of perceived motion cueing quality and offline MCA optimization.},
keywords = {continuous subjective ratings, curve driving, driving simulators, model predictive control, motion cueing},
}
Download .bib file
TY - CONF
TI - Sensitivity Analysis of an MPC-based Motion Cueing Algorithm for a Curve Driving Scenario
AU - Ploeg, Joost R. Van Der
AU - Cleij, Diane
AU - Pool, Daan M.
AU - Mulder, Max
AU - Bülthoff, Heinrich H.
C1 - Antibes, France
C3 - Proceedings of the Driving Simulation Conference 2020 Europe VR
DA - 2020/09/09
PY - 2020
SP - 37
EP - 44
LA - en-US
PB - Driving Simulation Association
L2 - https://proceedings.driving-simulation.org/proceeding/dsc-2020/sensitivity-analysis-of-an-mpc-based-motion-cueing-algorithm-for-a-curve-driving-scenario
ER -
Download .ris file
Cite this article
Terms and Conditions for Downloading Driving Simulation Proceedings papers:
By downloading a scientific paper from proceedings.driving-simulation.org, you agree to the following terms and conditions:
- Personal Use Only:
The scientific paper provided on this website is solely for personal, educational, and non-commercial use. You may download and use the paper for your own reference and research purposes only.
- No Reproduction or Distribution:
You may not reproduce, distribute, transmit, publish, or otherwise make the paper available to any third party in any form, whether for commercial or non-commercial purposes, without the express written consent of the Driving Simulation Association.
- Copyright and Ownership:
The scientific paper is protected by copyright laws and is the intellectual property of the respective authors and publishers. All rights not expressly granted herein are reserved.
- Citation and Attribution:
If you use the scientific paper for research, presentations, or any other non-commercial purposes, you must provide appropriate citation and attribution to the original authors as per academic standards.
- No Modification:
You may not modify, alter, or adapt the content of the scientific paper in any way.
- Disclaimer:
The Driving Simulation Association makes no representations or warranties regarding the accuracy, completeness, or suitability of the scientific paper for any particular purpose. The paper is provided as-is, without any warranties, express or implied. The Driving Simulation Association reserves the right to terminate or restrict access to the scientific paper at any time and without notice.