M3-Former: פלטפורמת טרנספורמר מרוב-מודלים עם מיקסטור-אקספרטים לתחזית נתיבי ספינות בטווח זמן ארוך

תקציר מקורי באנגליתarXiv:2609.10559v1 Announce Type: new Abstract: 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 intr