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：精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极具挑战。现有方法往往难以保持全局方向一致性，导致在长时间外推时产生轨迹漂移或违背物理规律。为解决此问题，我们提出一种基于语义关键点条件约束的轨迹建模框架，通过以表征航行意图的高层“下一关键点”为条件来预测未来轨迹。该框架将长时程预测分解为全局语义决策与局部运动建模，从而将未来轨迹的支撑集有效限制在语义可行的子空间内。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AIS数据的大量实验表明，所提方法在长航行时段、方向精度及细粒度轨迹预测方面均持续优于现有最优方法。