用时空图扩散模型预测船舶轨迹，更好捕捉多模式航行行为。
STGDPM:Vessel Trajectory Prediction with Spatio-Temporal Graph Diffusion Probabilistic Model
- 将船舶交互建模为动态图，替代传统状态聚合方法。
- 在真实AIS数据上实现更优的多模态轨迹预测性能。
- 适合关注海上安全与智能导航的研究者和工程师。
船舶轨迹预测是保障海上交通安全、避免碰撞的关键环节。由于船舶行为存在固有不确定性，轨迹预测系统需采用多模态方法来准确建模潜在的未来运动状态。然而，现有方法难以全面捕捉行为的多模态特性。为此，我们提出将船舶交互建模为动态图，取代依赖船舶状态的传统聚合技术。通过利用扩散模型天然的多模态能力，将轨迹预测任务视为运动不确定性逐步扩散的逆过程，逐步消除潜在航行区域中的不确定性，最终生成目标轨迹。综上，我们首次将时空图（STG）与扩散模型结合用于船舶轨迹预测。基于真实自动识别系统（AIS）数据的大量实验验证了该方法的优越性。
原文摘要 · Abstract (English)
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.
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