SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction

2026-01-26

AI Summary

Long-term vessel trajectory prediction is highly susceptible to complex navigational behaviors and environmental disturbances, often resulting in inconsistent global heading and trajectory drift. To address this challenge, this work proposes a two-stage framework centered on semantic next key points. First, a pretraining-finetuning strategy is employed to estimate key point priors from historical AIS data, effectively capturing high-level navigational intent. These semantic key points then serve as conditional constraints for local trajectory generation, restricting predictions to a semantically feasible subset of trajectories. The proposed approach significantly improves directional accuracy and trajectory plausibility over extended horizons, outperforming existing methods on real-world AIS datasets, with particularly notable gains in fine-grained trajectory modeling.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: State Estimation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Accurate long-horizon vessel trajectory prediction remains challenging due to compounded uncertainty from complex navigation behaviors and environmental factors. Existing methods often struggle to maintain global directional consistency, leading to drifting or implausible trajectories when extrapolated over long time horizons. To address this issue, we propose a semantic-key-point-conditioned trajectory modeling framework, in which future trajectories are predicted by conditioning on a high-level Next Key Point (NKP) that captures navigational intent. This formulation decomposes long-horizon prediction into global semantic decision-making and local motion modeling, effectively restricting the support of future trajectories to semantically feasible subsets. To efficiently estimate the NKP prior from historical observations, we adopt a pretrain-finetune strategy. Extensive experiments on real-world AIS data demonstrate that the proposed method consistently outperforms state-of-the-art approaches, particularly for long travel durations, directional accuracy, and fine-grained trajectory prediction.

Problem

• long-horizon trajectory prediction
• vessel navigation
• directional consistency
• trajectory drift
• navigational intent

Innovation

• semantic key-point conditioning
• long-horizon trajectory prediction
• next key point (NKP)
• vessel navigation intent
• pretrain-finetune strategy