2023/03/21 by Huimin Qiang, Qiang, Huimin, Zhiyuan Guo +5 · 1 citation
Computer Science · Engineering · Social Sciences · #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG) #Maritime Navigation and Safety #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2303.11540
openalex publication_date 2023/03/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Incorporating the dynamics knowledge into the model is critical for achieving accurate trajectory prediction while considering the spatial and temporal characteristics of the vessel. However, existing methods rarely consider the underlying dynamics knowledge and directly use machine learning algorithms to predict the trajectories. Intuitively, the vessel's motions are following the laws of dynamics, e.g., the speed of a vessel decreases when turning a corner. Yet, it is challenging to combine dynamic knowledge and neural networks due to their inherent heterogeneity. Against this background, we propose MSTFormer, a motion inspired vessel trajectory prediction method based on Transformer. The contribution of this work is threefold. First, we design a data augmentation method to describe the spatial features and motion features of the trajectory. Second, we propose a Multi-headed Dynamic-aware Self-attention mechanism to focus on trajectory points with frequent motion transformations. Finally, we construct a knowledge-inspired loss function to further boost the performance of the model. Experimental results on real-world datasets show that our strategy not only effectively improves long-term predictive capability but also outperforms backbones on cornering data.The ablation analysis further confirms the efficacy of the proposed method. To the best of our knowledge, MSTFormer is the first neural network model for trajectory prediction fused with vessel motion dynamics, providing a worthwhile direction for future research.The source code is available at https://github.com/simple316/MSTFormer.