  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.
