Vessel trajectory prediction with a Gated Spatio-Temporal Graph Aggregation Network

Abstract

Trajectory prediction is a critical technique for enhancing the efficiency of marine traffic control and maintaining the vessel navigation safety. In general, accurate trajectory prediction is examined in both temporal and spatial dimensions, including dynamic motion pattern modeling and capturing spatial interactions between vessels. Existing approaches have made some headway on the aforementioned concerns, but they tend to disregard the dynamic interactions between vessels and only evaluate static properties. To address this challenge, this paper proposes a novel Gated Spatio-Temporal Graph Aggregation Network (G-STGAN) for vessel trajectory prediction. Specifically, Spatial Gated Encoder (SGE), a variant of graph convolutional networks, is presented to describe the spatial interactions between vessels. In the temporal dimension, we designed a temporal gated encoder (TGE) to effectively fuse short-term and long-term temporal dependencies. Furthermore, the spatial and temporal features from the SGE and TGE modules, respectively, are aggregated by using a temporal convolutional network (TCN) to perform downstream prediction tasks. Experiments on three real-world AIS datasets demonstrate that G-STGAN can achieve competitive prediction performance in terms of accuracy and robustness.