# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 1 Introduction
### Literature Review
#### 1.2.3 Related Research on Ship Behavior Mining  
In recent years, there has been widespread attention paid to mining ship behavior patterns from vessel spatiotemporal information. Ship behavior mining can provide important theoretical support for research in areas like intelligent ships, smart navigation aids, and intelligent shipping monitoring. Trajectory compression algorithms, which retain key points of a trajectory to identify specific behaviors, are often used for ship behavior mining and analysis. The most classic trajectory compression algorithm is the Douglas-Peucker (DP) algorithm, which has been widely applied. For example, [PERSON] and [PERSON] [23] proposed a maritime traffic pattern recognition method based on the DP algorithm and density clustering. [PERSON] et al. [24] used the DP algorithm to compress ship AIS trajectory data and then applied multidimensional density-based noise clustering to extract maritime traffic routes. [PERSON] et al. [25] developed an Adaptive-Threshold Douglas-Peucker (ADP) algorithm based on the DP algorithm. Compared with the traditional DP algorithm, the ADP algorithm's distance threshold is determined by the varying critical threshold of each trajectory point. As a result, the ADP algorithm adaptively adjusts the distance threshold based on the variations in the compressed ship trajectory. [PERSON] et al. [26] employed the AF-DP algorithm to compress ship trajectory data and extract core trajectories. By comparing the behavioral similarity between the predicted trajectory and the preset threshold core trajectory, they determined whether a trajectory was anomalous or whether the vessel's operation was abnormal. In addition, the sliding window algorithm, which relies on its advantage of local processing, has also been widely applied to time series analysis. [PERSON] et al. proposed an improved sliding window algorithm for data mining methods related to characteristic routes and anomaly detection. This method filters AIS data based on ship and maritime traffic characteristics and clusters the characteristic routes using clustering algorithms. [PERSON] et al. [27] addressed the dynamic motion problem in ship trajectories by extracting motion characteristics using the DP algorithm and the sliding window algorithm based on heading and speed information. At the same time, the development of machine learning and neural networks offers new research approaches for ship collision avoidance behavior mining. [PERSON] et al. [28] proposed a Convolutional Neural Network-Ship Movement Modes Classification (CNNSMMC) algorithm, which involves designing a Ship Movement Image Generation and Labelling (SMIGL) algorithm. This method converts ship AIS trajectories into various motion images, fully leveraging the classification capabilities of CNN. A comparative study with several benchmark classification algorithms demonstrated that the model performs better in classifying AIS-based ship behavior data. Similarly, [PERSON] and [PERSON] et al. [29] utilized the powerful capabilities of neural networks to propose a ship trajectory prediction framework based on Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) models. This framework extracts the intrinsic features of ship trajectories through forward and backward processing and evaluates ship trajectory identification performance in both single-ship and multi-ship scenarios, achieving satisfactory trajectory prediction results. [PERSON] et al. [30] similarly employed a Bidirectional Long Short-Term Memory Recurrent Neural Network (Bi-LSTM-RNN) to develop a real-time ship behavior prediction model suitable for Automatic Identification System (AIS) time-series feature recognition and online parameter adjustment. The bidirectional structure enhances the correlation between historical and future data, thereby improving prediction accuracy. In summary, regarding ship encounters and behavior mining, previous research has mostly identified specific characteristic behaviors of ships through trajectory compression.