Vessel trajectory prediction is important for intelligent shipping, maritime surveillance, and navigation safety. However, existing public maritime AIS resources are often limited by inconsistent forecasting protocols, uneven data quality, and the lack of benchmark-ready contextual annotations, which hinder fair comparison and context-aware modeling. To address this gap, we present EnvShip-Bench, a unified benchmark for short-term vessel trajectory prediction built from large-scale raw AIS data from the Danish Maritime Authority (DMA) and NOAA through a common processing pipeline. EnvShip-Bench adopts a standardized forecasting protocol with 10 minutes of observation, 10 minutes of prediction, and 20-second sampling in vessel-centric local metric coordinates. Beyond the large-scale core benchmark, it provides a quality-first compact subset for efficient and reproducible experimentation, together with synchronized environmental and nearby-vessel context extensions. As a result, EnvShip-Bench supports trajectory-only, environment-aware, and interaction-aware forecasting under a unified evaluation framework. Extensive benchmark statistics and analysis demonstrate that EnvShip-Bench offers a standardized, extensible, and context-aware foundation for maritime trajectory forecasting research.

翻译：船舶轨迹预测对智能航运、海上监控及航行安全具有重要意义。然而，现有公开的海事AIS资源常因预测协议不一致、数据质量参差不齐及缺乏基准级上下文标注而受限，这阻碍了公平比较与上下文感知建模。为弥补这一不足，我们提出了EnvShip-Bench——一个基于丹麦海事局（DMA）及美国国家海洋和大气管理局（NOAA）大规模原始AIS数据，通过统一处理流程构建的短期船舶轨迹预测标准化基准。EnvShip-Bench采用标准化预测协议：以船舶为中心的局部公制坐标下，观测时长10分钟、预测时长10分钟、采样间隔20秒。除大规模核心基准外，该基准还提供优先保证质量的紧凑子集以支持高效且可复现的实验，并同步提供环境及邻近船舶上下文扩展。因此，EnvShip-Bench可在统一评估框架下支持纯轨迹、环境感知及交互感知预测。广泛的基准统计与分析表明，EnvShip-Bench为海事轨迹预测研究提供了标准化、可扩展且具备上下文感知能力的基础平台。