用语义关键点约束，提升长时间船舶轨迹预测准确性
SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction
- 通过预测未来语义关键点来引导长时轨迹生成
- 在真实AIS数据上，长时预测误差降低18.7%，方向一致性提升23%
- 适合需要高精度航行规划的航运与海事分析场景
由于复杂航行行为和环境因素带来的累积不确定性，准确预测长时间跨度的船舶轨迹仍具挑战。现有方法常因全局方向不一致导致长期外推时轨迹漂移或不合理。为此，本文提出一种基于语义关键点的轨迹建模框架，通过条件化高阶未来下一关键点（NKP）来捕捉航行意图。该方法将长时预测分解为全局语义决策与局部运动建模，有效将未来轨迹支持范围限制在语义可行子集中。为高效从历史观测中估计NKP先验，采用预训练-微调策略。在真实世界AIS数据上的大量实验表明，该方法在长时行程、方向准确性和细粒度轨迹预测方面持续优于当前最优方法。
原文摘要 · Abstract (English)
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.
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