A Dynamic Spatio-Temporal Refinement Network for Multi-Vessel Trajectory Prediction

Abstract

In maritime transportation, accurate prediction of AIS-based vessel trajectory has a significant impact on collision-free vessel trajectory prediction. Due to the dense and highly dynamic marine navigation traffic, it is a great challenge to predict the vessels' trajectories. Currently, the mainstream trajectory prediction methods are based on recurrent neural networks ignoring spatial-temporal interaction between vessels. In order to fully learn the structural features of complex trajectories and the interaction features between motion targets in complex scenarios, we propose a Dynamic Spatial-Temporal Refinement Network (DSTNet). Our model introduces the states refinement module and Temporal Transformer. Specifically, the vessel attention factor and motion gate are integrated to implement the interaction of vessel spatial trajectory information. Moreover, Temporal Transformer is used to obtain global traffic characteristics, extracting the impact of traffic-agents beyond the specified spatial limits and integrate spatio-temporal data. The results of our proposed method on a real-world Automatic Identification System (AIS) dataset show 18.8% and 25% improvement in ADE and FDE metrics compared to other methods.