发表机构
Northeastern University（东北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对复杂海洋环境下船舶轨迹预测问题，引入NaviAIS数据集并提出NaviLane框架，通过轨迹-地图联合编码、离散宏动作码本及模块优化评估等方法，提升船舶轨迹预测性能，优于现有基线。
AI 中文摘要
复杂海洋环境中的船舶轨迹预测对交通管理、碰撞预警等至关重要。基于AIS的学习方法虽发展迅速，但现有数据集存在诸多问题。为此引入NaviAIS，它在统一时间窗和局部坐标系内组织多船历史-未来轨迹，提供多种地图表示。在此基础上提出NaviLane框架，先进行轨迹-地图联合编码，再用离散宏动作码本生成多模态候选轨迹，经模块优化和评估。实验表明NaviLane在单模态和多模态设置中均优于基线。
英文摘要
Vessel trajectory prediction in complex maritime environments is essential for traffic management, collision warning, route planning, and autonomous navigation. Although AIS-based learning methods have progressed rapidly, existing datasets are often released as raw message streams or irregular time series, with inconsistent sampling rates, noisy observations, heterogeneous coordinate systems, and non-unified scenario protocols. Most public AIS resources also lack structured representations of navigational lanes, waterway geometry, and navigable-region constraints, limiting reproducible, environment-aware forecasting. To address this, we introduce NaviAIS, a standardized scenario-level AIS dataset for vessel trajectory prediction. It organizes multi-vessel historical-future trajectories within unified temporal windows and local coordinate systems, and provides rasterized navigable maps, vectorized lane priors, lane graphs, and structured map representations. Compared with existing datasets, it jointly supports vectorized lanes, multi-scenario coverage, vectorized maps, open accessibility, and processed trajectories. Built on this dataset, we propose NaviLane, a hierarchical macro-action framework for map-aware prediction. NaviLane first performs trajectory-map joint encoding for a unified scene representation, then uses a discrete macro-action codebook to generate multimodal candidates coarse-to-refined. A residual refinement module improves local geometric and dynamical consistency, and a world-model-based consequence-aware evaluator ranks candidates by interaction risk and environmental feasibility. Experiments show NaviLane outperforms representative baselines in both single-modal and multimodal settings, confirming the value of structured navigational priors, hierarchical multimodal generation, and consequence-aware evaluation.
