提出可解释船位轨迹预测框架，提升复杂场景适应性与决策透明度。
Unified Multimodal Vessel Trajectory Prediction with Explainable Navigation Intention
- 构建持续意图树与动态瞬时意图模型，融合历史轨迹与条件变分自编码器
- 在真实AIS数据上，ADE与FDE指标显著优于现有方法
- 明确揭示每条轨迹背后的航行意图，适合需可解释性的海事智能系统
船舶轨迹预测是智能航运系统的基础。在复杂海况中，对快速行为变化的短时预测已推动多模态轨迹预测（MTP）成为重要研究方向。然而，现有方法普遍存在场景适应性差、可解释性不足的问题。为此，本文提出统一的多模态轨迹预测框架，引入可解释的航行意图，分为持续性与瞬时性两类。通过历史轨迹构建持续意图树，利用条件变分自编码器（CVAE）建模动态瞬时意图，并采用非局部注意力机制保持全局场景一致性。在真实自动识别系统（AIS）数据集上的实验表明，该方法在多种场景下均具广泛适用性，且在ADE和FDE指标上均有显著提升。此外，通过显式揭示各预测轨迹背后的航行意图，显著增强可解释性。
原文摘要 · Abstract (English)
Vessel trajectory prediction is fundamental to intelligent maritime systems. Within this domain, short-term prediction of rapid behavioral changes in complex maritime environments has established multimodal trajectory prediction (MTP) as a promising research area. However, existing vessel MTP methods suffer from limited scenario applicability and insufficient explainability. To address these challenges, we propose a unified MTP framework incorporating explainable navigation intentions, which we classify into sustained and transient categories. Our method constructs sustained intention trees from historical trajectories and models dynamic transient intentions using a Conditional Variational Autoencoder (CVAE), while using a non-local attention mechanism to maintain global scenario consistency. Experiments on real Automatic Identification System (AIS) datasets demonstrates our method's broad applicability across diverse scenarios, achieving significant improvements in both ADE and FDE. Furthermore, our method improves explainability by explicitly revealing the navigational intentions underlying each predicted trajectory.
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