EnvShip-Bench: An Environment-Enhanced Benchmark for Short-Term Vessel Trajectory Prediction

2026-06-13

AI Summary

Existing research on vessel trajectory prediction suffers from inconsistent evaluation protocols, low-quality data, and a lack of contextual annotations, hindering fair comparisons and context-aware modeling. To address these limitations, this work establishes the first standardized benchmark for short-term maritime trajectory prediction by integrating environmental factors—such as weather conditions—and interactions with nearby vessels, built upon large-scale raw AIS data from the Danish Maritime Authority and NOAA. The benchmark employs a unified data processing pipeline, a vessel-centric local coordinate system, a 10-minute observation/prediction window, and a 20-second sampling interval, and provides both a large-scale core dataset and a high-quality compact subset. Empirical results demonstrate that this benchmark offers a scalable, reproducible, and context-rich foundation for evaluating vessel trajectory prediction models.

Abstract

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.

Problem

• vessel trajectory prediction
• AIS data
• benchmark
• context-aware modeling
• maritime surveillance

Innovation

• trajectory prediction
• maritime benchmark
• environmental context
• AIS data
• context-aware modeling