Feature-Wise Linear Modulation-Conditioned Transformer for AIS Trajectory Restoration Incorporating Vessel Geometry and Maneuvering Characteristics

Automatic Identification System (AIS) data are widely used for maritime traffic analysis, vessel monitoring, and intelligent navigation systems. However, missing trajectory segments caused by irregular reporting intervals and communication disruptions can degrade the continuity and reliability of vessel trajectories. This study proposes a vessel-condition-aware AIS trajectory restoration framework that combines Cubic Spline-based preliminary restoration with a Feature-wise Linear Modulation (FiLM)-conditioned Transformer and gated bounded residual correction. For AIS sequences of 200 points, the central 60 points are treated as missing segments, and Cubic Spline interpolation is first applied to generate a preliminary trajectory. The proposed model then predicts residual corrections by incorporating vessel-specific condition information, including hull geometry features, maneuvering-related descriptors, and appendage-related proxy descriptors, into Transformer representations through FiLM modulation. Experimental results show that the proposed method reduces the average Euclidean distance (AED) from 145.43 m to 134.20 m and the maximum displacement error (MDE) from 286.03 m to 269.45 m compared with Cubic Spline interpolation, corresponding to improvements of 7.73% and 5.80%, respectively. The proposed method also achieves lower AED than recurrent residual correction models, including BiLSTM. Condition-shuffling analysis further demonstrates that vessel-specific condition information contributes to improved AIS trajectory restoration performance.