用多模态知识增强框架提升船舶轨迹预测精度
A Multi-Modal Knowledge-Enhanced Framework for Vessel Trajectory Prediction
- 引入语言模型迁移轨迹上下文知识，应对数据采样不规则
- 基于运动学知识渐进学习复杂轨迹模式，提升泛化能力
- 在两个数据集上比现有方法精度提升12.08%~17.86%，适合航运安全应用
准确的船舶轨迹预测有助于提升航行安全、优化航线规划并保护环境。然而，现有方法面临全球AIS系统数据采样时间间隔不规则及船舶运动模式复杂的挑战，导致模型学习与泛化困难。为此，我们提出多模态知识增强框架（MAKER）以改善船舶轨迹预测。为应对不规则采样问题，MAKER采用大语言模型引导的知识迁移（LKT）模块，有效利用预训练语言模型传递轨迹特定上下文知识。为增强复杂轨迹模式的学习能力，MAKER引入基于知识的自适应渐进学习（KSL）模块，利用运动学知识在训练中逐步整合复杂模式，实现自适应学习与更强泛化性能。在两个船舶轨迹数据集上的实验结果表明，MAKER可使当前最优方法的预测精度提升12.08%至17.86%。
原文摘要 · Abstract (English)
Accurate vessel trajectory prediction facilitates improved navigational safety, routing, and environmental protection. However, existing prediction methods are challenged by the irregular sampling time intervals of the vessel tracking data from the global AIS system and the complexity of vessel movement. These aspects render model learning and generalization difficult. To address these challenges and improve vessel trajectory prediction, we propose the multi-modal knowledge-enhanced framework (MAKER) for vessel trajectory prediction. To contend better with the irregular sampling time intervals, MAKER features a Large language model-guided Knowledge Transfer (LKT) module that leverages pre-trained language models to transfer trajectory-specific contextual knowledge effectively. To enhance the ability to learn complex trajectory patterns, MAKER incorporates a Knowledge-based Self-paced Learning (KSL) module. This module employs kinematic knowledge to progressively integrate complex patterns during training, allowing for adaptive learning and enhanced generalization. Experimental results on two vessel trajectory datasets show that MAKER can improve the prediction accuracy of state-of-the-art methods by 12.08%-17.86%.
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