Chalak Qazani, Mohammad Reza, Asadi, Houshyar, Najdovski, Zoran, Alsanwy, Shehab, Zakarya, Muhammad, Alam, Furqan, Ouakad, Hassen M, Lim, Chee Peng, and Nahavandi, Saeid (2024) High-fidelity learning-based motion cueing algorithm by bypassing worst-case scenario-based tuning technique. Cognitive Robotics, 4. pp. 116-127.

Preview

PDF (Published Version) - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Preview

Abstract

The motion cueing algorithm (MCA) enhances the realism of simulator driving experiences by generating vehicle motions within platform limitations. Existing MCAs are typically tuned for worst-case scenarios, limiting their efficiency for medium or slow driving motions. This study proposes a comprehensive MCA unit using learning-based models to overcome this problem and efficiently utilise the simulator workspace for all driving scenarios. Data samples are regenerated to cover various motion signal levels, and three classical washout filters are tuned to extract optimal motion signals. A multilayer perceptron (MLP) is trained with these extracted datasets, forming an AI-based MCA that provides high-fidelity driving motions for any scenario while optimising the platform workspace. Simulink/MATLAB is used for modelling and evaluation. Results demonstrate the proposed model's superior performance, with lower motion sensation errors, a higher correlation between sensed motion signals, and more efficient platform workspace usage.

Item ID:	86716
Item Type:	Article (Research - C1)
ISSN:	2667-2413
Keywords:	Motion-planning; Motion cueing algorithm; Simulator platform; Learning methods; Multilayer perceptron; Simulation; Worst-case tuning
Copyright Information:	© 2024 The Authors. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Date Deposited:	10 Sep 2025 01:41
FoR Codes:	40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400705 Control engineering @ 55% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 45%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220407 Human-computer interaction @ 60% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 40%
	More Statistics