# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 1 Introduction
### Literature Review
#### 1.2.2 Related Research on Detecting Ship Encounter Scenarios  
Generally, most studies on ship-to-ship collision risks are based on the identification of potential collision scenarios, specifically focusing on the recognition of vessel encounter situations. [PERSON] and [PERSON] [13] were the first to propose the concept of collision diameter, introducing a method that comprehensively considers factors like traffic flow and speed to assess vessels with potential collision risks. [PERSON] et al. [14] applied this concept to establish criteria for identifying near-collision scenarios, using AIS data near the Portuguese coast to recognize and distinguish head-on, crossing, and overtaking near-collision situations. However, the collision diameter method only detects situations where vessels are nearly in physical contact, which may lead to missed detections when used as a standard. To address this limitation, the concept of the \"ship safety domain\" was developed. [PERSON] and [PERSON] (1971) [15] described it as \"the effective water area surrounding a vessel that a ship operator wishes to keep free from other vessels or fixed objects\". In the detection of vessel encounter situations, most scholars consider the intrusion of other vessels into the ship's safety domain as the primary judgment criterion. [PERSON] et al. [16] proposed using the speed dispersion index, acceleration and deceleration, and the number of overlapping fuzzy ship domains as collision risk indicators to quantitatively evaluate collision risks in vessels based on AIS data from the Singapore Strait. [PERSON] et al. [17], building on the concept of the ship safety domain, applied the nonlinear velocity obstacle (NL-VO) method. They used the criterion of whether a vessel's speed falls into the velocity obstacle set to determine the presence of collision risk, thereby detecting vessel encounter scenarios from a process-oriented perspective. Additionally, there are methods based on indicators to detect vessel encounter situations using metrics like relative distance, relative speed, the Time to Closest Point of Approach (TCPA), and the Distance to Closest Point of Approach (DCPA) to define recognition criteria for encounter scenarios. [PERSON] et al. [18] performed a statistical analysis of vessels involved in collisions using AIS data and established a linear regression model for CPA based on vessel size, speed, and course. They proposed a dynamic collision  
Figure 1: Traditional encounter scenario division model. (**a**)The Division Model Based on the Relative Bearing Angle. (**b**)Limitations of the Division Model Based on the Relative Bearing Angle. risk monitoring method based on SAMSON. [PERSON] et al. [19] optimized the Vessel Conflict Ranking Operator (VCRO) model by considering vessel domains, vessel size, and the concept of Minimum Distance to Collision (MDTC). The updated model was able to better distinguish different levels of collision risk in vessel encounter situations based on AIS data. [PERSON] et al. [20] proposed a ship encounter identification and classification method based on AIS data. By excluding multi-ship encounter scenarios, adopting grouping and data point selection strategies, and integrating clustering techniques, the method classifies encounter types into distinct categories. It further analyzes decision making parameters and extracts valuable insights from the data. In [PERSON] et al. [21], based on AIS data and complex network theory, an automatic identification and analysis model for multi-ship encounter situations was proposed. This model constructs an encounter graph network to comprehensively characterize the spatiotemporal dynamics of multi-ship encounters. By employing fuzzy logic and a weighted PageRank algorithm, it ranks collision risks, enabling more efficient collaborative collision avoidance decision making. Furthermore, [PERSON] et al.