Mix&Fix-Net：一种用于AIS与视觉衍生船舶数据的双阶段轨迹预测模型

中文摘要 AI 辅助

船舶轨迹预测对海事安全和事故预防至关重要。现有大多数轨迹预测模型因自动识别系统（Automatic Identification System, AIS）数据的精度和可获取性而依赖该数据，但小型船舶大多不配备AIS，导致存在显著的监测缺口。为解决该问题，我们提出Mix&Fix-Net，一种基于双阶段混合器的轨迹预测模型，旨在处理同时来自AIS和（非AIS）视觉数据的船舶轨迹时间序列数据。该架构集成了初级轨迹预测器（Primary Trajectory Predictor）与残差轨迹调整器（Residual Trajectory Adjuster），可实现更精细的轨迹预测。此外，我们引入了一个源自网络摄像头流的新型基于视频的数据集，从中提取船舶轨迹以代表非AIS数据。在AIS和非AIS数据集上针对六项指标（均方误差、平均绝对误差、对称平均绝对百分比误差、最终位移误差、弗雷歇距离、平均欧氏距离）开展的广泛评估表明，Mix&Fix-Net在大多数指标和数据集上均持续优于现有基线模型。

英文摘要

Vessel trajectory prediction is critical for maritime safety and accident prevention. While most existing trajectory prediction models rely on Automatic Identification System (AIS) data due to its precision and availability, small vessels mostly operate without AIS, resulting in a significant monitoring gap. To address this, we propose Mix&Fix-Net, a dual-stage mixer-based trajectory prediction model designed to handle vessel trajectory time-series data derived from both AIS and (non-AIS) vision data. Our architecture integrates a Primary Trajectory Predictor with a Residual Trajectory Adjuster, enabling more refined trajectory prediction. Additionally, we introduce a new video-based dataset derived from webcam streams, from which vessel trajectories are extracted to represent non-AIS data. Extensive evaluations on both AIS and non-AIS datasets across six metrics (mean squared error, mean absolute error, symmetric mean absolute percentage error, final displacement error, Frechet distance, and average Euclidean distance) demonstrate that Mix&Fix-Net consistently outperforms existing baselines across most metrics and datasets.

发表机构

• University of South Florida（南佛罗里达大学）
• Istanbul University（伊斯坦布尔大学）

机构由 AI 辅助整理，请以论文原文为准。