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.

翻译：准确的长时程船舶轨迹预测因复杂航行行为与环境因素叠加的不确定性而持续面临挑战。现有方法往往难以保持全局方向一致性，导致在长时程外推时出现轨迹漂移或不合逻辑的现象。为解决此问题，我们提出一种语义关键点条件化的轨迹建模框架，该框架通过以捕捉航行意图的高层"下一关键点"为条件来预测未来轨迹。此方法将长时程预测分解为全局语义决策与局部运动建模，从而有效地将未来轨迹的支撑集限制在语义可行的子集内。为从历史观测中高效估计下一关键点的先验分布，我们采用预训练-微调策略。基于真实世界AIS数据的大量实验表明，所提方法在长航行时段、方向准确性及细粒度轨迹预测方面均持续优于现有先进方法。