Alsanwy, Shehab, Asadi, Houshyar, Chalak Qazani, Mohamad Reza, Al-Ashmori, Mohammed, Mohamed, Shady, Nahavandi, Darius, Alqumsan, Ahmad Abu, Al-Serri, Sari, Jalali, Seyed Mohammad, and Nahavandi, Saeid (2022) Prediction of Vehicle Motion Signals for Motion Simulators Using Long Short-Term Memory Networks. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. pp. 34-39. From: SMC 2022: IEEE International Conference on Systems, Man, and Cybernetics, 9-12 October 2022, Prague, Czech Republic.

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Abstract

Driving simulators are utilized for many applications including basic driver training, human factor studies, human-machine interaction, and vehicle prototyping in automobile industries. The main purpose of using driving simulator is to provide realistic driving experience. Since simulator platforms have physical limitations, Motion Cueing Algorithms (MCAs) are used to generate driving sensation for the simulator user while considering the simulator's physical and dynamical constraints. When using a model predictive control (MPC)-based MCA, the principle of MPC is leveraged to predict an optimized future behavior of the simulator where a series of control actions is developed across a defined future horizon using the explicitly specified process model. Corresponding to the pre-positioning or time-varying reference MPC, it is crucial to predict the future vehicle motion signals for the simulator accurately. The existing methods for predicting vehicle motion signals do not excel in predicting time-series of a long sequence due to the missing feedback loop or limited memory size. To address this issue, the Long Short-Term Memory (LSTM) model is developed to predict motion signals using Python. The performance of LSTM is compared with those from different traditional methods using several measurements criteria, which include the root mean squared error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (r). The results indicate that LSTM outperforms RNN by producing more accurate motion allowing the MCA to deliver realistic motion sensations, the LSTM model can be employed in a wide range of applications including autonomous vehicles trajectory prediction, and other prediction problems.

Item ID:	87046
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-6654-5258-8
Copyright Information:	© 2022 IEEE.
Funders:	Australian Research Council (ARC)
Date Deposited:	04 Sep 2025 03:30
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 70% 40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400706 Field robotics @ 30%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 100%
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