M3-Former：基于混合专家模型的多模态Transformer用于长期船舶轨迹预测
M3-Former: Multimodal Transformer with Mixture-of-Experts for Long-Term Vessel Trajectory Prediction
- University of Chinese Academy of Sciences（中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对船舶轨迹预测中的多模态性、语义利用不足和长期误差累积问题，提出M3-Former框架，融合大语言模型语义先验与双粒度MoE架构，在丹麦AIS数据上将4小时预测ADE和FDE分别降低4.4%和5.1%。
AI中文摘要：
为应对船舶轨迹预测中行为多模态性、语义利用有限以及长期误差累积等挑战，本文提出M3-Former，一种由大语言模型（LLMs）增强的多模态轨迹预测框架。该框架将船舶静态属性和航行意图作为长期轨迹建模的语义先验。具体而言，构建了一个统一的多模态表示空间，其中静态语义信息由预训练的LLM编码，并通过自注意力机制与动态轨迹特征对齐。为联合捕捉全局航线规划和局部运动变化，引入了双粒度混合专家（MoE）架构，其中序列级专家建模全局航行趋势，而令牌级专家细化细粒度的操纵行为。此外，设计了转向加权交叉熵损失，以缓解稀疏转向样本的长尾分布，并提高关键操纵场景下的预测精度。在真实丹麦AIS数据集上的实验表明，M3-Former在1至4小时的预测范围内始终优于最先进的基线方法。在4小时预测任务中，与最强基线相比，所提方法将平均位移误差（ADE）和最终位移误差（FDE）分别降低了4.4%和5.1%。定性和消融分析进一步验证了语义融合有效减少了长期轨迹漂移，而双粒度MoE提高了复杂水道和航线分支场景下的鲁棒性。所提框架建立了一种语义引导的分层预测范式，其中高层航行意图和局部运动动态被联合建模，以实现稳健的长期船舶轨迹预测。
英文摘要：
To address the challenges of behavioral multimodality, limited semantic utilization, and long-term error accumulation in vessel trajectory prediction, this paper proposes M3-Former, a multimodal trajectory prediction framework enhanced by large language models (LLMs). The proposed framework incorporates vessel static attributes and navigational intent as semantic priors for long-term trajectory modeling. Specifically, a unified multimodal representation space is constructed, in which static semantic information is encoded by a pre-trained LLM and aligned with dynamic trajectory features through self-attention. To jointly capture global route planning and local motion variations, a dual-granularity Mixture-of-Experts (MoE) architecture is introduced, where sequence-level experts model global navigation trends and token-level experts refine fine-grained maneuvering behaviors. In addition, a Steering-Weighted Cross-Entropy loss is designed to alleviate the long-tail distribution of sparse turning samples and improve prediction accuracy in critical maneuvering scenarios. Experiments on a real-world Danish AIS dataset demonstrate that M\textsuperscript{3}-Former consistently outperforms state-of-the-art baselines across prediction horizons from 1 to 4 hours. In the 4-hour prediction task, the proposed method reduces Average Displacement Error (ADE) and Final Displacement Error (FDE) by 4.4\% and 5.1\%, respectively, compared with the strongest baseline. Qualitative and ablation analyses further verify that semantic fusion effectively reduces long-term trajectory drift, while the dual-granularity MoE improves robustness in complex waterways and route-branching scenarios. The proposed framework establishes a semantic-guided hierarchical prediction paradigm, in which high-level navigational intent and local motion dynamics are jointly modeled for robust long-term vessel trajectory forecasting.
