基于条件注意力的上下文感知船舶轨迹预测
Context-Informed Ship Trajectory Prediction via Conditional Attention
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中文总结 AI 辅助
该研究提出Conditional Informer架构，通过条件注意力机制编码船舶与环境的物理依赖，结合Modality Masking策略，在AIS与ERA5数据上提升了船舶轨迹预测准确率并降低了传感器故障时的误差。
中文摘要 AI 辅助
长期船舶轨迹预测是海事安全与自主导航的核心能力。尽管近期基于Transformer的架构延长了预测 horizon，但它们主要依赖历史运动学状态，将船舶运动视为孤立系统。现实中，海事航行受天气等外在因素深刻影响，且受船舶静态特征约束。现有多模态方法本质上对状态与上下文的联合分布建模，将环境变量视为同等特征，而非编码船舶动力学对环境条件的定向物理依赖。本研究提出Conditional Informer，一种新型编解码架构，将轨迹预测建模为条件生成任务。我们采用专用的条件注意力机制，其中船舶状态通过交叉注意力显式查询环境上下文，编码天气调制船舶动力学（而非由船舶动力学生成天气）的物理先验。此外，为应对真实世界数据的间歇性，我们引入Modality Masking训练策略，以防止传感器故障时的灾难性性能下降。在AIS与ERA5数据上的大量实验表明，当上下文可用时，我们的方法在预测准确率上比运动学基线和基于拼接的基线高出15.4%。关键的是，Modality Masking可防止捷径学习，与无约束模型相比，将故障时的误差降低了近一个数量级。
英文摘要
Long-term ship trajectory prediction is a fundamental capability for maritime safety and autonomous navigation. While recent Transformer-based architectures have improved forecasting horizons, they predominantly rely on historical kinematic states, treating vessel motion as an isolated system. In reality, maritime navigation is profoundly modulated by extrinsic factors like weather and constrained by static vessel characteristics. Existing multimodal approaches fundamentally model the joint distribution over states and contexts, treating environmental variables as peer features rather than encoding the directional physical dependence of vessel dynamics on environmental conditions. In this work, we propose the Conditional Informer, a novel encoder-decoder architecture that formulates trajectory prediction as a conditional generation task. We employ a dedicated Conditional Attention mechanism where the vessel state explicitly queries environmental contexts through cross-attention, encoding the physical prior that weather modulates - but is not generated by - vessel dynamics. Furthermore, to address the intermittency of real-world data, we introduce a Modality Masking training strategy to prevent catastrophic degradation during sensor fallback. Extensive experiments on AIS and ERA5 data demonstrate that our approach outperforms kinematic and concatenation-based baselines by 15.4% in prediction accuracy when context is available. Crucially, Modality Masking prevents shortcut learning, reducing fallback error by nearly an order of magnitude compared to unconstrained models.
发表机构
- Carnegie Mellon University（卡内基梅隆大学）
- Advanced Technology Laboratories Lockheed Martin（洛克希德·马丁公司先进技术实验室）
机构由 AI 辅助整理，请以论文原文为准。
