## 2 Literature Review  
Due to issues related to receiving equipment, transmitting devices, and network transmission, raw AIS data often contain some level of noise, necessitating preprocessing for effective utilization. Numerous efforts have been made by researchers in the field of AIS data processing. [PERSON] proposed a trajectory similarity measurement and clustering method to divide scenes into semantic regions, aiming to mitigate the negative effects caused by AIS [3]. [PERSON] conducted research on the automated processing of AIS data and developed an automated processing platform [4]. This platform achieved functionality for updating AIS data, vessel static information, route information, and voyage information. [PERSON] proposed an anomaly detection method based on AIS trajectories. They integrated the kinematic information of vessels in AIS data and reduced trajectory anomalies through data preprocessing, motion estimation, and error clustering [5]. [PERSON] introduced a preprocessing model consisting of data cleaning, trajectory extraction, and trajectory compression stages. This method effectively removes excess noise and reduces the size of trajectory data [6]. [PERSON] proposed preprocessing methods such as navigation data extraction, abnormal data handling, and missing data interpolation to address issues such as AIS data loss, inaccuracy, and incomplete preservation of dynamic navigation features [7]. [PERSON] presented a two-step AIS processing method involving trajectory interpolation and the application of trajectory point detection methods to interpolated and observed AIS messages [8]. This approach enhances trajectory resolution effectively. [PERSON] proposed an unsupervised knowledge mining framework for generating maritime traffic networks [9]. This framework integrates AIS data preprocessing algorithms, enabling the generation of high-quality, spatio-temporally continuous maritime trajectory data from low-quality inputs. While the aforementioned methods demonstrate good performance in processing individual AIS data values for single vessels, they are time-consuming and lack real-time capabilities when handling a large volume of ship data simultaneously. Moreover, they often do not address the issue of uneven time intervals between trajectory points, making them less suitable for the input requirements of trajectory prediction models. Traditional trajectory prediction models can be primarily categorized into two classes: statistical learning methods and machine learning methods. Deep learning, as a branch of machine learning, has gained prominence in recent years, and deep learning-based prediction methods have become the mainstream in trajectory prediction research. [PERSON] proposed a deep learning prediction model based on the Bi-GRU network, which outperformed LSTM and standard GRU network prediction models in terms of lower error and higher accuracy [10]. [PERSON] proposed an optimized Attention-LSTM neural network for dynamic navigation prediction, and validated the accuracy and robustness of the model through a simulation analysis [7]. The results demonstrate that this method achieves the high-precision prediction of vessel longitude, latitude, heading, and speed. [PERSON] extended the deep learning framework for trajectory prediction tasks by exploring how recurrent encoder-decoder neural networks can not only predict but also generate corresponding prediction uncertainty for both aleatoric and epistemic uncertainties using a Bayesian model [11]. [PERSON] introduced a novel trajectory prediction model called PESO, comprising parallel encoders, ship-oriented decoders, and Semantic Location Vectors (SLVs) [12]. Parallel encoders aim to capture more information in feature representations, while ship-oriented decoders use SLV to guide predictions and better represent the spatial correlations of historical trajectory points. [PERSON] presented a high-precision multi-step prediction method called MP-LSTM, which combines current trajectory data and historical trajectory data [13].