| Popis: |
The prediction of vessel trajectories plays a crucial role in ensuring maritime safety and reducing maritime accidents. Substantial progress has been made in trajectory prediction tasks by adopting sequence modeling methods, containing recurrent neural networks (RNNs) and sequence-to-sequence networks (Seq2Seq). However, (1) most of these studies focus on the application of trajectory information, such as the longitude, latitude, course, and speed, while neglecting the impact of differing vessel features and behavioral preferences on the trajectories. (2) Challenges remain in acquiring these features and preferences, as well as enabling the model to sensibly integrate and efficiently express them. To address the issue, we introduce a novel deep framework VEPO-S2S, consisting of a Multi-level Vessel Trajectory Representation Module (Multi-Rep) and a Feature Fusion and Decoding Module (FFDM). Apart from the trajectory information, we first defined the Multi-level Vessel Characteristics in Multi-Rep, encompassing Shallow-level Attributes (vessel length, width, draft, etc.) and Deep-level Features (Sailing Location Preference, Voyage Time Preference, etc.). Subsequently, Multi-Rep was designed to obtain trajectory information and Multi-level Vessel Characteristics, applying distinct encoders for encoding. Next, the FFDM selected and integrated the above features from Multi-Rep for prediction by employing both a priori and a posteriori mechanisms, a Feature Fusion Component, and an enhanced decoder. This allows the model to efficiently leverage them and enhance overall performance. Finally, we conducted comparative experiments with several baseline models. The experimental results demonstrate that VEPO-S2S is both quantitatively and qualitatively superior to the models. |
