Dynamic-learning spatial-temporal Transformer network for vehicular trajectory prediction at urban intersections

Geng, Maosi; Chen, Yong; Xia, Yingji; Chen, Xiqun (Michael)

doi:10.1016/j.trc.2023.104330

Cited by 7 publications

(1 citation statement)

References 54 publications

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“…Predicting the drift trajectory of a stranded AUV is a time-series prediction problem. At present, models such as recurrent neural networks (RNNs) [13,14], long short-term memory networks (LSTMs) [15,16], and transformers [17,18] and their derivative versions have proven to be able to handle timeseries prediction problems stably and have been widely used in research in recent years. Ma [16] and Tang [19] used the LSTM model for ship trajectory prediction, which promotes the rapid development of ship autonomous navigation technology.…”

Section: Neural Network Model-based Approachmentioning

confidence: 99%

GAT-ABiGRU Based Prediction Model for AUV Trajectory

Zhao,

Zhang,

et al. 2024

Applied Sciences

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Autonomous underwater vehicles (AUVs) are critical components of current maritime operations. However, because of the complicated marine environment, AUVs are at significant risk of being lost, and such losses significantly impact the continuity and safety of aquatic activities. This article suggests a methodology for forecasting the trajectory of lost autonomous underwater vehicles (AUVs) based on GAT-ABiGRU. Firstly, the time-series data of the AUV are transformed into a graph structure to represent the dependencies between data points. Secondly, a graph attention network is utilized to capture the spatial features of the trajectory data, while an attention-based bidirectional gated recurrent unit network learns the temporal features of the trajectory data; finally, the predicted drift trajectory is obtained. The findings show that the GAT-ABiGRU model outperforms previous trajectory prediction models, is highly accurate and robust in drift trajectory prediction, and presents a new method for forecasting the trajectory of wrecked AUVs.

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