# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 2 Related Work  
### Risk Collision Analysis  
With the rapid development of the maritime industry, the safety of maritime transportation has attracted the attention of people in related fields, and the risk analysis methods of maritime transportation have been widely studied [9]. Risk collision analysis, as a broader scenario of trajectory prediction, plays an important role in improving the safety of maritime navigation. Ship collision risk analysis methods can be divided into probabilistic model-based methods, geometric-based methods, and artificial neural network-based methods [10]. Approach based on Geometry. Geometry-based methods can be divided into two categories: those based on the Closest Point of Approach (CPA) and those based on the Safe Domain (SD). The CPA method analyzes collision risk by predicting the closest possible point of encounter between ships in the future, considering parameters such as their current position, heading, and speed [11; 12; 13]. By calculating the shortest distance between ships and the time required to reach that distance, potential collision risks can be identified, allowing for the implementation of actions to avoid collisions. On the other hand, SD-based methods calculate potential collisions by establishing a navigational safety zone around a ship. However, these methods are highly sensitive to parameterization and may not yield highly accurate results. Approach based on probabilistic model. The probabilistic model-based approach is primarily based on support vector machines (SVMs) and Bayesian networks. SVM-based methods calculate the probability of ship collisions by considering the ship's motion characteristics and environmental factors, enabling quantitative risk assessment. [PERSON] et al. (2019) implemented SVM-based probability computation by using the Safe Domain (SD) of the owner ship (OS) and the target ship (TS) as input features. Building upon this foundation, [PERSON] et al. (2019) developed a fuzzy quaternion ship domain (FQSD) model for ships, which enables the calculation of ship collision risk by solving the maximum interval of SD (MISD) and the violation degree of SD (VDSD). The method based on Bayesian Networks (BNs) (BNs) (BNs) (BNs) (BNs) is an effective means of modeling factors affecting accidents and nonlinear causal relationships. [PERSON] et al. (2019) integrated ship collision simulation results and expert knowledge into Bayesian Networks to obtain the probability of specific collision occurrences. [PERSON] et al. (2019) also proposed a ship collision risk analysis method based on the K2 algorithm for Bayesian networks (BNs), predicting probabilities of various types of maritime accidents along Maritime Silk Road through maritime accident reports. Approach based on artificial neural network. The traditional approach ([PERSON] et al., 2019) to analyzing collision risk using neural networks focuses on predicting ship positions to aid in collision avoidance. To enhance the prediction of risk index, [PERSON] et al. (2020) developed a convolutional neural network (CNN) for ship collision avoidance based on expert knowledge and Automatic Identification System (AIS) trajectory data. The network initially calculates the original ship collision risk based on the ships' motion characteristics and distance between them, then adjusts this value using expert experience. Finally, the corrected data and remotely sensed images are used as inputs for predicting the risk index. In order to reduce the learning time of the network, reinforcement learning is applied in collision risk analysis. By combining model-based and model-free algorithms with asynchronous advantage actor-critic (A3C) ([PERSON] et al., 2019) algorithms with existing models, significant reductions in model learning time can be achieved. For example, [PERSON] et al. (2020) combined a long short-term memory neural network (LSTM), Q-learning, and the A3C algorithm to improve efficiency in the reinforcement learning process.