Vessel trajectory prediction is a critical component for ensuring maritime traffic safety and avoiding collisions. Due to the inherent uncertainty in vessel behavior, trajectory prediction systems must adopt a multimodal approach to accurately model potential future motion states. However, existing vessel trajectory prediction methods lack the ability to comprehensively model behavioral multi-modality. To better capture multimodal behavior in interactive scenarios, we propose modeling interactions as dynamic graphs, replacing traditional aggregation-based techniques that rely on vessel states. By leveraging the natural multimodal capabilities of diffusion models, we frame the trajectory prediction task as an inverse process of motion uncertainty diffusion, wherein uncertainties across potential navigational areas are progressively eliminated until the desired trajectories is produced. In summary, we pioneer the integration of Spatio-Temporal Graph (STG) with diffusion models in ship trajectory prediction. Extensive experiments on real Automatic Identification System (AIS) data validate the superiority of our approach.

本研究解决了现有船舶轨迹预测方法缺乏全面建模行为多模态性的问题。作者提出了一种新的方法，将互动场景建模为动态图，并利用扩散模型的自然多模态能力，将轨迹预测任务框架设定为运动不确定性扩散的逆过程。通过在真实的自动识别系统（AIS）数据上进行广泛实验，验证了该方法的优越性。

STGDPM：基于时空图扩散概率模型的船舶轨迹预测