Motion-Decoupled Dual-Stream Representation Learning for AIS-Based Vessel Trajectory Prediction

Abstract

Automatic Identification System (AIS)-based vessel trajectory prediction is essential for maritime traffic management and navigation safety. Existing deep learning methods typically model vessel motion within a unified temporal representation space, which may entangle long-term navigation trends with local maneuvering behaviors. However, vessel trajectories inherently exhibit heterogeneous dynamics, including steady route evolution and non-stationary maneuver perturbations. To address this issue, this paper proposes MD-EDTCNFormer, a motion-decoupled dual-stream framework for vessel trajectory prediction. A Global Navigation Dynamics Encoder is designed to capture dominant route-level temporal evolution from raw AIS sequences, while a Residual Maneuver Dynamics Encoder explicitly models maneuver-related local perturbations through state transition residual representations. In addition, a state-adaptive motion aggregation mechanism is introduced to dynamically balance global navigation dependencies and local maneuver-aware dynamics under different navigation states. Depthwise separable temporal convolution and efficient channel attention are further integrated to suppress redundant temporal-channel coupling and emphasize dynamically dominant motion cues. Experiments on a real-world AIS dataset from the Zhoushan coastal area demonstrate the effectiveness of the proposed framework under coastal traffic conditions, and show improvements in prediction accuracy and trajectory stability compared with representative baseline methods.

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