SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction
This addresses the challenge of maintaining global directional consistency in vessel trajectory prediction for maritime applications, representing a domain-specific incremental improvement.
The paper tackles the problem of long-horizon vessel trajectory prediction by proposing a semantic-key-point-conditioned framework that decomposes prediction into global semantic decision-making and local motion modeling. The method consistently outperforms state-of-the-art approaches on real-world AIS data, particularly for long travel durations, directional accuracy, and fine-grained trajectory prediction.
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.