A novel method for ship trajectory prediction in complex scenarios based on spatio-temporal features extraction of AIS data

Ship trajectory prediction plays a key role in the early warning and safety of maritime navigation. Ship pilots must have a complete grasp of the future trajectories of ships within a certain period of time when moving the ship to effectively avoid collisions. However, the accuracy of ship trajectory prediction is a significant issue that needs to be resolved at present. In this paper, we propose a ship trajectory prediction model covering spatio-temporal awareness graph attention network (GAT) based on long short-term memory network (LSTM) to predict the future trajectories of ships in complex scenarios, named STPGL model, which adopts the design of encoder-decoder structure. The historical encoder uses an LSTM to extract the kinematic sequence features of each ship from historical trajectories in the temporal dimension. The interactive encoder summarizes the interaction features between different ships through GAT in the spatial dimension. Then, the two features are fused and fed into the decoder module to infer the future trajectories of ships. The experimental results demonstrate that STPGL model can effectively improve the prediction accuracy of short-term, medium-term and long-term ship trajectory. It has excellent performance and has a certain reference value for the advancement of unmanned ship collision avoidance.