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This study introduces Mix&Fix-Net, a dual-stage trajectory prediction model that leverages both AIS and vision-derived data to enhance vessel trajectory predictions, particularly for small vessels lacking AIS. The model's architecture features a Primary Trajectory Predictor and a Residual Trajectory Adjuster, which together improve the accuracy of trajectory forecasts. Evaluation results indicate that Mix&Fix-Net significantly outperforms existing models across multiple metrics, addressing a critical gap in maritime safety monitoring.
Mix&Fix-Net bridges the monitoring gap for small vessels by integrating AIS and vision data, achieving superior trajectory prediction accuracy.
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.
