In addressing the challenges of trajectory prediction in multi-ship interaction scenarios and aiming to improve the accuracy of multi-ship trajectory prediction, this paper proposes a multi-ship trajectory prediction model, GL-STGCNN. The GL-STGCNN model employs a ship interaction adjacency matrix extraction module to obtain a more reasonable ship interaction adjacency matrix. Additionally, after obtaining the distribution of predicted trajectories using the model, a model predictive control trajectory correction method is introduced to enhance the accuracy and reasonability of the predicted trajectories. Through quantitative analysis of different datasets, it was observed that GL-STGCNN outperforms previous prediction models with a 31.8% improvement in the average displacement error metric and a 16.8% improvement in the final displacement error metric. Furthermore, trajectory correction through model predictive control shows a performance boost of 44.5% based on the initial predicted trajectory distribution. While GL-STGCNN excels in multi-ship interaction trajectory prediction by reasonably modeling ship interaction adjacency matrices and employing trajectory correction, its performance may vary in different datasets and ship motion patterns. Future work could focus on adapting the model’s ship interaction adjacency matrix modeling to diverse environmental scenarios for enhanced performance.

This study proposes an integrated framework for multi-ship trajectory prediction and motion planning based on joint learning, which significantly enhances the overall performance in multi-ship interaction scenarios by combining the global information from trajectory prediction with the local optimization capabilities of motion planning. In the trajectory prediction task, the proposed MTSGCN model achieves an average performance improvement of 26.3% compared to the Rain model, while the joint multi-task learning strategy yields a 26.9% performance gain over single-task learning, demonstrating the effectiveness of the multi-ship interaction adjacency matrix extraction module. In the motion planning task, the MTSGCN model outperforms the TFT model with an average performance improvement of 13.2%, whereas the MTSGCN-T model without joint learning experiences an 11.2% performance degradation. Furthermore, the study reveals that ship speed and heading decisions are influenced by multiple factors, with the inertial effect of historical parameters being the most significant. Additionally, the feature distributions of different datasets have a substantial impact on model performance.