AIS-LLM：一个用于海事轨迹预测、异常检测与碰撞风险评估的统一框架，具备可解释预测能力
AIS-LLM: A Unified Framework for Maritime Trajectory Prediction, Anomaly Detection, and Collision Risk Assessment with Explainable Forecasting
AI总结：
提出AIS-LLM框架，将时间序列AIS数据与大语言模型集成，通过跨模态对齐在单一端到端系统中同时实现船舶轨迹预测、异常检测和碰撞风险评估，并生成可解释的态势简报，有效提升海上交通管理效率。
摘要：
随着海上交通的增长以及自动识别系统（AIS）的强制实施，基于AIS数据的海上交通分析任务（如船舶轨迹预测、异常检测和碰撞风险评估）的重要性和多样性正在迅速增加。然而，现有方法倾向于单独处理这些任务，难以全面考虑复杂的海事情况。为解决这一局限，我们提出了一种新颖的框架AIS-LLM，它将时间序列AIS数据与大语言模型（LLM）集成在一起。AIS-LLM由用于处理AIS序列的时间序列编码器、基于LLM的提示编码器、用于时间序列数据与文本提示间语义对齐的跨模态对齐模块，以及基于LLM的多任务解码器组成。该架构能够在单一端到端系统中同时执行三项关键任务：轨迹预测、异常检测和船舶碰撞风险评估。实验结果表明，AIS-LLM在各项单独任务上均优于现有方法，验证了其有效性。此外，通过综合分析任务输出来生成态势总结和简报，AIS-LLM展现了实现更智能、更高效海上交通管理的潜力。
英文摘要：
With the increase in maritime traffic and the mandatory implementation of the Automatic Identification System (AIS), the importance and diversity of maritime traffic analysis tasks based on AIS data, such as vessel trajectory prediction, anomaly detection, and collision risk assessment, is rapidly growing. However, existing approaches tend to address these tasks individually, making it difficult to holistically consider complex maritime situations. To address this limitation, we propose a novel framework, AIS-LLM, which integrates time-series AIS data with a large language model (LLM). AIS-LLM consists of a Time-Series Encoder for processing AIS sequences, an LLM-based Prompt Encoder, a Cross-Modality Alignment Module for semantic alignment between time-series data and textual prompts, and an LLM-based Multi-Task Decoder. This architecture enables the simultaneous execution of three key tasks: trajectory prediction, anomaly detection, and risk assessment of vessel collisions within a single end-to-end system. Experimental results demonstrate that AIS-LLM outperforms existing methods across individual tasks, validating its effectiveness. Furthermore, by integratively analyzing task outputs to generate situation summaries and briefings, AIS-LLM presents the potential for more intelligent and efficient maritime traffic management.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
