EnvShip-Bench: An Environment-Enhanced Benchmark for Short-Term Vessel Trajectory Prediction
arXiv:2606.15240v1 Announce Type: new 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 th
The increasing sophistication of AI models and the criticality of maritime domain awareness are driving demand for robust, standardized benchmarks for trajectory prediction.
Improved vessel trajectory prediction enhances maritime safety, surveillance, and intelligent shipping, with implications for global supply chains and national security.
The availability of EnvShip-Bench provides a standardized and context-rich dataset, enabling more reliable comparison and development of AI models for marine navigation.
- · AI researchers in maritime applications
- · Shipping companies
- · Maritime surveillance agencies
- · Port authorities
- · Developers relying on inconsistent historical datasets
More accurate short-term vessel trajectory predictions become achievable.
Enhanced navigation safety reduces incidents and improves shipping efficiency.
The development of highly autonomous marine vessels accelerates due to improved predictive capabilities.
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Read at arXiv cs.LG