A prediction of time series driving motion scenarios using LSTM and ESN
Qazani, Mohammad Reza Chalak; Tabarsinezhad, Farzin; Asadi, Houshyar; Lim, Chee Peng; Arogbonlo, Adetokunbo; Alsanwy, Shehab; Mohamed, Shadi; Rostami, Mehrdad; Nahavandi, Saeid (2022-11-18)
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.
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https://urn.fi/URN:NBN:fi-fe2023033134175
Tiivistelmä
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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