Mar 21, 2018 · In this paper, we use three types of physiological signals from the driver to predict lane changes before the event actually occurs.
The experimental results showed that, in comparison to other state-of-the-art models, our MTS-GCNN performs significantly better in terms of prediction accuracy ...
In our MTS-GCNN model, we present a new structure learning algorithm in training stage. The algorithm exploits the covariance structure over multiple time ...
Multivariate time series prediction of lane changing behavior using deep ...
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Multivariate time series prediction of lane changing behavior using deep neural network ; Journal: Applied Intelligence, 2018, № 10, p. 3523-3537 ; Publisher: ...
Three types of physiological signals from the driver are used to predict lane changes before the event actually occurs, showing that the proposed MTS-GCNN ...
Multivariate time series prediction of lane changing behavior using deep neural network. - Equivital. Skip to Navigation. Equivital. Industries. First response ...
Multivariate time series prediction of lane changing behavior using deep neural network - 2018 ; Research Area: Data Mining ; Keywords: ; Author(s) Name: Jun Gao, ...
Bibliographic details on Multivariate time series prediction of lane changing behavior using deep neural network.
To capture the stochastic time series of lane-changing behavior, this study proposes a temporal convolutional network (TCN) to predict the long-term lane- ...
Missing: Multivariate | Show results with:Multivariate
Apr 28, 2021 · The proposed model achieves the prediction accuracy of 93.5% and improves the prospective time of prediction by about 2.1 s on average. 1.