## 2 Related Work
### Ship Trajectory Prediction  
In recent years, significant progress has been made in ship trajectory prediction research. Various methods, such as Graph Attention Networks (GAT), Long Short-Term Memory Networks (LSTM), and Generative Adversarial Networks (GAN), have been combined with spatial feature extraction, temporal feature learning, and improved algorithms to enhance prediction accuracy and real-time performance, providing crucial support for maritime traffic management and ship safety. [PERSON] et al. (2018) proposed a ship trajectory prediction method based on GAT and LSTM, combining spatial feature extraction andtemporal feature learning to achieve high-precision ship trajectory prediction, which is significant for maritime traffic control and safe navigation. [PERSON] et al. [20] introduced a novel multi-ship trajectory prediction model called GL-STGCNN, incorporating a ship interaction adjacency matrix extraction module and a Model Predictive Control (MPC) trajectory correction method, significantly improving trajectory prediction accuracy and rationality in multi-ship interaction scenarios. [PERSON] et al. [21] proposed a ship trajectory prediction model based on an improved Bi-directional LSTM (Bi-LSTM), which introduced the Rectified Adaptive Moment Estimation and Lookahead optimization algorithms to enhance model generalization and prediction accuracy. Experimental results demonstrated the superior performance of this model in ship trajectory prediction. [PERSON] et al. [22] presented a ship trajectory prediction and analysis framework based on the Bi-LSTM model, utilizing Automatic Identification System (AIS) data. The model's prediction performance was validated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), showing satisfactory ship trajectory prediction results in both single-ship and multi-ship scenarios. [PERSON] et al. [23] proposed a ship trajectory prediction method based on a hybrid K-Nearest Neighbor (KNN) and LSTM model. By considering the differences in ship density across various sea areas, the KNN algorithm was optimized and combined with spatio-temporal features to achieve high-precision ship trajectory prediction. [PERSON] et al. [24] introduced a ship trajectory prediction method based on a Differential LSTM (D-LSTM) network, which effectively improved trajectory prediction accuracy in complex waterways with frequent maneuvers by integrating dynamic time-varying features from time series data and differential variables, significantly reducing training time. [PERSON] et al. [25] proposed a ship trajectory prediction method based on an improved LSTM and GAN, significantly enhancing the accuracy and real-time performance of ship trajectory prediction, which is crucial for maritime traffic management and infrastructure protection. [PERSON] et al. [26] developed a method for identifying ship maneuvering motion models based on least squares and combined ocean current velocity impacts to establish an extreme short-term ship trajectory prediction model. By real-time online correction of current velocity information, the model achieved high-precision ship trajectory prediction under different ocean current conditions. [PERSON] et al. [27] introduced an improved ship trajectory prediction model based on a Multi-Head Attention mechanism and Bi-directional Gated Recurrent Unit (MHA-BiGRU). Experiments verified its effectiveness in achieving high-precision, real-time ship trajectory prediction, providing support for maritime traffic services and ship navigation. [PERSON] et al. [28] proposed a ship trajectory prediction method based on a Sequence-to-Sequence model using Convolutional LSTM. The method improved ship trajectory prediction accuracy through preprocessing and feature extraction, validated with real AIS data. [PERSON] [29] developed a ship trajectory prediction model based on Adaptive Chaotic Differential Evolution optimized Support Vector Regression (ACDE-SVR).