# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 2 Related Work
### Trajectory Prediction  
Currently, trajectory prediction methods can be primarily divided into shallow learning-based and deep learning-based approaches ([PERSON] et al., 2020). Shallow learning-based methods have been around for a longer time and show certain effectiveness in handling simple trajectory prediction tasks. However, their application is limited due to the lack of evaluation standards and limited adaptability in complex scenarios. With the widespread application of deep learning techniques in various fields, more researchers are leveraging the advantages of deep learning in capturing long-term dependencies and complex patterns in data and applying them to the handling of trajectory prediction problems. Trajectory prediction method based on shallow learning. Early trajectory prediction methods usually combine kinematic models and Bayesian filters or their extensions to make predictions by propagating the current state to the future state ([PERSON] et al., 2020). This method is simple and easy to implement, but the prediction accuracy is limited in complex scenarios. To describe nonlinear motion, [PERSON] et al. (2019) proposed a switched linear dynamical system model, and [PERSON] et al. (2020) proposed a dynamic Bayesian network model that considers social and physical constraints for path prediction under specific constraints. These methods have certain advantages in dealing with complex problems, but they need to consume a lot of computing resources, and it is difficult to make full use of some additional scenarios and information. The rapid advancements in machine learning have facilitated the application of tracking algorithms to enhance trajectory prediction models, such as the Kalman Filter (KF), Markov Model (MM), and Gaussian Process (GP) [28; 29; 30]. KF has high short-term forecasting accuracy, but its long-term forecasting ability is limited. MM is sensitive to trajectory fluctuations and is not suitable for medium and long-term trajectory prediction. GP is well-suited for predicting noisy point trajectory data as it effectively mitigates the issue of insufficiently discrete trajectory data and accurately represents the statistical characteristics of the trajectory distribution. However, constructing a GP is quite complex and requires a long computation time. Although the trajectory prediction method of shallow learning has achieved some results in the early stage, the model based on kinematics combined with a machine learning algorithm has certain limitations. The prior assumptions of these models may constrain overall performance and present challenges when dealing with complex scenarios and large data sets. Due to the lack of specific scene information, lack of motion feature information, complex model construction, and limited samples of large data sets, there is a certain gap between the prediction effect of these methods and the actual situation. Trajectory prediction method based on deep learning. Compared with the shallow learning complex model construction, the trajectory prediction method based on deep learning does not need a fixed mathematical model. This method is based on the construction of a network and relies on large-scale data sets to learn a more reasonable mapping relationship to better deal with complex trajectory data. In recent years, with the rise of deep learning, a variety of temporal prediction models have emerged, among which Recurrent Neural Networks (RNN) and their variants, such as Long Short Term Memory (LSTM), have emerged. LSTM and Gate Recurrent Unit (GRU) have achieved remarkable success in trajectory prediction. [PERSON] et al. [31] extended LSTM for human trajectory and solved the problem that the performance of the Seq2 Seq sequence model decreases with the increase of input sequence; they also verified the effectiveness of Seq2 Seq sequence model in trajectory modeling and motion pattern prediction.