融合雷达与视觉数据，提升小船轨迹预测精度
Mix&Fix-Net: A Dual-Stage Trajectory Prediction Model for AIS and Vision-Derived Vessel Data
- 双阶段架构：先预测再修正，融合AIS与视觉数据
- 在6项指标上优于现有模型，尤其对无AIS小船效果显著
- 适合海上监控、智能航运系统开发者参考
船舶轨迹预测对航海安全和事故预防至关重要。现有模型多依赖高精度且易获取的自动识别系统（AIS）数据，但小型船只大多未配备AIS，造成监测盲区。为此，本文提出Mix&Fix-Net，一种基于双阶段混合器的轨迹预测模型，可处理来自AIS及非AIS视觉数据的船舶时序轨迹。模型采用主轨迹预测器与残差轨迹修正器结合的结构，实现更精细的预测。此外，我们构建了一个基于网络摄像头流的新视频数据集，从中提取船舶轨迹以代表非AIS数据。在包含六项评估指标（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、Frechet距离、平均欧氏距离）的广泛测试中，Mix&Fix-Net在多数数据集和指标上均优于现有基线模型。
原文摘要 · Abstract (English)
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.
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