University of Oulu

M. R. C. Qazani et al., "A Prediction of Time Series Driving Motion Scenarios Using LSTM and ESN," 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic, 2022, pp. 1592-1599, doi: 10.1109/SMC53654.2022.9945220.

A prediction of time series driving motion scenarios using LSTM and ESN

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Author: Qazani, Mohammad Reza Chalak1; Tabarsinezhad, Farzin2; Asadi, Houshyar1;
Organizations: 1Institute for Intelligent Systems Research and Innovation Deakin University, Geelong, Australia
2The Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
3Centre of Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland
4Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2023033134175
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-03-31
Description:

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.

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Series: IEEE International Conference on Systems, Man, and Cybernetics
ISSN: 1062-922X
ISSN-E: 2577-1655
ISSN-L: 1062-922X
ISBN: 978-1-6654-5258-8
ISBN Print: 978-1-6654-5259-5
Pages: 1592 - 1599
DOI: 10.1109/SMC53654.2022.9945220
OADOI: https://oadoi.org/10.1109/SMC53654.2022.9945220
Host publication: Conference Proceedings : IEEE International Conference on Systems, Man and Cybernetics
Conference: IEEE international conference on systems, man, and cybernetics
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
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