Chalak Qazani, Mohamad Reza, Tabarsinezhad, Farzin, Asadi, Houshyar, Lim, Chee Peng, Arogbonlo, Adetokunbo, Alsanwy, Shehab, Mohamed, Shadi, Rostami, Mehrdad, and Nahavandi, Saeid (2022) A Prediction of Time Series Driving Motion Scenarios Using LSTM and ESN. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. pp. 1592-1599. From: SMC 2022: IEEE International Conference on Systems, Man, and Cybernetics, 9-12 October 2022, Prague, Czech Republic.

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Abstract

The motion signals are generated for a simulator user based on the visual understanding of the environment using virtual reality. In this respect, a motion cueing algorithm (MCA) is employed to reproduce the motion signals based on the real driving motion scenarios. Advanced MCAs are required to predict precise driving motion scenarios. Nonetheless, investigations on effective methods for predicting the driving motion scenarios accurately are limited. Current state-of-the-art studies mainly focus on the averaged motion signals from several simulator users pertaining to a specific map or from feedforward neural network and non-linear autoregressive. The existing methods are unable to yield precise predictions of the driving scenarios. In this research, the echo state network and long short-term memory models are employed for the first time in MCA to forecast the driving motion signals. Our evaluation proves the efficiency of our proposed methods in comparison with existing methods.

Item ID:	87043
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-6654-5258-8
Copyright Information:	© 2022 IEEE
Date Deposited:	04 Sep 2025 02:06
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 70% 40 ENGINEERING > 4002 Automotive engineering > 400203 Automotive mechatronics and autonomous systems @ 30%
SEO Codes:	27 TRANSPORT > 2703 Ground transport > 270302 Autonomous road vehicles @ 40% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 60%
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