Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models
Long-horizon maritime trajectory prediction is important for shipping management, logistics planning, and maritime risk analysis, yet month-level forecasting remains insufficiently studied. Existing deep learning methods mainly focus on short- and mid-term coordinate extrapolation and often struggle to preserve route feasibility and destination correctness over extended horizons. This paper investigates joint long-horizon vessel trajectory and destination forecasting with reasoning-capable large language models, and develops a Maritime LLM post-training framework based on Reinforcement Learning with Verifiable Reward (RLVR). An AIS-based benchmark is constructed with 60-day historical tra...
| Authors | Hongwei Wang, Miao Zhou, Fengde Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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