## 2 Literature Review
It uses LSTM neural network models to predict trajectory support points and then performs multi-step predictions using cubic spline interpolation for start, support, and endpoint prediction. This approach achieves high-precision predictions for both short and long distances while accurately predicting positions at each time step. [PERSON] proposed an improved Attention-LSTM model for dynamic navigation prediction, incorporating an Attention module for differential weighting and feature extraction from navigation sequences [7]. The LSTM module integrates information for final dynamic predictions. [PERSON] introduced the IS-STGCNN, a spatio-temporal graph convolutional model for trajectory prediction [14]. This model accounts for vessel interactions' impact on trajectory prediction, uses social sampling to update node vector representations, and employs MPC for prediction result correction. [PERSON] introduced a Hybrid Driven Framework, which fuses data-driven predictors with vessel motion constraints using a linear Kalman filter [15]. It uses a learning-based LSTM network as a data-driven predictor and employs the Kalman filter's physical-driven module to estimate future vessel trajectories using the data-driven predictor's sequential output as continuous measurements. [PERSON] proposed an LSTM-based interactive ship trajectory prediction framework known as QSD-LSTM, embedding Quaternion Ship Domain (QSD) [16]. QSD aids in avoiding unnecessary collisions between adjacent vessels during the trajectory prediction process. [PERSON] proposed a Context-aware Long Short-Term Memory (CLSTM) network [17]. Compared to the Long Short-Term Memory (LSTM) network, this framework improves prediction accuracy by 15.31%. [PERSON] proposed a deep generative model based on the conditional variational autoencoder framework to learn ship motion and predict future trajectories [18]. Experimental results indicate that this model outperforms baseline methods, including both kinematics-based and data-driven approaches. [PERSON] et al. [19] presented a trajectory prediction method based on Bi-GRU and trajectory direction vectors (TDVs) with an attention mechanism. They constructed a TDV, which correlates latitude and longitude with heading and speed, and used an adaptive attention mechanism to eliminate the influence of unreasonable predicted trajectory points in the trajectory. [PERSON] proposed a Deep Attention-Aware Spatio-Temporal Graph Convolutional Network (DAA-SGCN) based on AIS data to predict future vessel trajectories [20]. It mainly consists of three modules: the vessel trajectory motion information encoding, spatio-temporal feature extraction module, and trajectory prediction module. Based on extensive experiments, compared to the optimal baseline model, the predictive performance on _ADE_ and _FDE_ metrics improved by 74% and 69%, respectively. [PERSON] proposed a simple fusion-based RNN method for predicting vessel trajectories [21]. This approach can easily integrate other temporal features. Experimental results demonstrate that this method achieves state-of-the-art performance in three major coastal regions of the United States. When predicting the next three hours, it outperforms competing methods by 0.88 km. [PERSON] et al. [22] extracted five machine learning methods and seven deep learning methods from state-of-the-art literature reviews. These methods were employed to perform AIS data-based trajectory predictions in three representative busy coastal waters and compare their prediction performance in real-world scenarios. The performance of all twelve methods was evaluated and analyzed, providing a detailed exploration of the characteristics and effectiveness of these trajectory prediction methods. However, the paper did not conduct an in-depth optimization of the twelve aforementioned models. This is uncommon in practical applications. [PERSON] et al.