Incorporating prior knowledge of collision risk into deep learning networks for ship trajectory prediction in the maritime Internet of Things industry

Abstract

Artificial intelligence (AI) has played a key role in advancing autonomous navigation for unmanned ships, where ship trajectory prediction is crucial for ensuring maritime safety. As the shipping industry grows and the number of ships increases, especially with autonomous ships operating in complex environments, collision risks have become a major concern. Accurate trajectory prediction, supported by advanced AI techniques, is crucial for the safe operation of these ships. While current models predict ship trajectories with high precision using Automatic Identification System (AIS) data, they often fail to incorporate prior knowledge of collision risks and struggle to model ship interactions that could lead to collisions. To overcome these limitations, the DGCN-Transformer (Dynamic Graph Convolution Network-Transformer) model is proposed. This model enhances the accuracy and reliability of ship trajectory predictions by incorporating collision risk modeling into the prediction framework. It uses the Quaternion Ship Domain (QSD) to model potential collision scenarios, integrating an advanced understanding of ships' spatial and kinematic properties. The model integrates QSD-based prior knowledge into an advanced Graph Convolutional Network (GCN) for spatial modeling, while the Transformer component captures and analyzes temporal features, overcoming the limitations of traditional Long Short-Term Memory (LSTM) networks. Experiments with AIS data from Tianjin, Caofeidian, and Chengshanjiao ports demonstrate that the DGCN-Transformer model outperforms state-of-the-art models, significantly improving trajectory prediction accuracy. Specifically, at Tianjin Port, the DGCN-Transformer model reduces Final Displacement Error (FDE) by 36.1%, Maximum Displacement Error (MDE) by 15.4%, and Average Displacement Error (ADE) by 50% compared to the best baseline model, highlighting the model's effectiveness in enhancing the safety of autonomous ship navigation.