发表机构
Technical University of Denmark（丹麦技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对海上船舶长期轨迹预测，提出融合Informer编码器与多通道时间编码的TempTPI框架，利用ProbSparse注意力降低计算复杂度，在丹麦水域AIS数据上较TPTrans在5小时预测中MSE降低55%。
AI 中文摘要
准确的海上船舶长期轨迹预测对于安全和物流效率至关重要。尽管深度学习模型，特别是Transformer，在处理自动识别系统（AIS）数据方面显示出潜力，但它们常常面临自注意力机制的二次计算复杂度以及在延长预测时间跨度时精度下降的问题。本研究提出了TempTPI，一种新颖的预测框架，该框架将基于Informer的编码器与多通道时间编码机制相结合。Informer架构利用ProbSparse自注意力机制来降低计算开销并聚焦于最重要的依赖关系，而时间编码器则采用类似傅里叶的频率扩展来捕捉船舶行为中的周期性模式（小时、日和季节）。我们使用来自丹麦水域的AIS数据，将我们的模型与最先进的TPTrans架构进行评估。实验结果表明，TempTPI在1至5小时的预测窗口内始终优于现有方法。值得注意的是，在5小时的时间跨度上，所提出的模型在均方误差（MSE）上实现了55%的改进，为远程海上态势感知提供了稳健的解决方案。
英文摘要
Accurate long-term trajectory prediction for maritime vessels is essential for safety and logistical efficiency. While deep learning models, particularly Transformers, have shown promise in processing Automatic Identification System (AIS) data, they often struggle with the quadratic computational complexity of self-attention and the loss of accuracy over extended forecasting horizons. This study proposes TempTPI, a novel prediction framework that integrates an Informer-based encoder with a multi-channel temporal encoding mechanism. The Informer architecture leverages a ProbSparse self-attention mechanism to reduce computational overhead and focus on the most significant dependencies, while the temporal encoder utilizes Fourier-like frequency expansions to capture cyclic patterns (hourly, daily, and seasonal) in vessel behavior. We evaluate our model against the state-of-the-art TPTrans architecture using AIS data from Danish waters. Experimental results demonstrate that TempTPI consistently outperforms existing methods across prediction windows of 1 to 5 hours. Notably, at a 5-hour horizon, the proposed model achieves a 55% improvement in Mean Squared Error (MSE), offering a robust solution for long-range maritime situational awareness.
CommentsAccepted to IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2026. \c{opyright} 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media
