A hierarchical framework fuses a long-term intent predictor with a grid-based Spatio-Temporal Graph Transformer and environmental cross-modal attention to cut average displacement error by 25% over 10-hour horizons on Australian vessel data.
An attention mechanism model based on positional encoding for the prediction of ship maneuvering motion in real sea state,
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Hierarchical Two-Stage Framework for Environment-Aware Long-Horizon Vessel Trajectory Prediction
A hierarchical framework fuses a long-term intent predictor with a grid-based Spatio-Temporal Graph Transformer and environmental cross-modal attention to cut average displacement error by 25% over 10-hour horizons on Australian vessel data.