Problem
Research questions and friction points this paper is trying to address.
Vessel Trajectory Prediction
AIS Data
Non-AIS Vision Data
Monitoring Gap
Mix&Fix-Net: A Dual-Stage Trajectory Prediction Model for AIS and Vision-Derived Vessel Data
🤖 AI Summary
本文提出Mix&Fix-Net，一种双阶段轨迹预测模型，结合AIS和视觉数据解决小船监控不足的问题，提高了船舶轨迹预测精度。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dual-Stage
Mixer-based Model
Primary Trajectory Predictor
Residual Trajectory Adjuster
Non-AIS Vision Data
🔎 Similar Papers
No similar papers found.
