# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 4 Discussion  
This study aims to identify the collision avoidance behaviors of ships by analyzing their trajectories and understanding their behaviors in different encounter scenarios. To identify the timing of collision avoidance maneuvers in ship trajectories, a Curvature-Based Ship Collision Avoidance Behavior Recognition algorithm has been developed. The proposed algorithm eliminates data interference in the AIS transmission process through corresponding turning factors, and it uses a recovering factor to determine whether a ship's turn is part of a collision avoidance maneuver. This makes the algorithm exhibit high accuracy and robustness in comparison with traditional recognition algorithms in experiments.  
Despite the good performance of the model in validation, there are still some limitations. First, the model relies heavily on AIS data, and the accuracy and reliability of AIS data directly affect the recognition results of the model. When AIS data are severely missing or contain significant noise, even with interpolation methods, such as cubic spline interpolation, the recognition performance of the model may still be significantly impacted. Additionally, the model's parameter settings (such as the sliding window size and cur  
Figure 17: Statistical results of case waterway identification.  
Figure 18: Collision avoidance behavior identification results under different encounter scenarios in the case waterway. (**a**) Head-on Collision Avoidance Data. (**b**) Crossing Collision Avoidance Data. (**c**) Overtaking Collision Avoidance Data.  
vature thresholds) need to be adjusted according to different water areas and ship types, and they lack general applicability. In the case of extremely complex encounter scenarios (e.g., multiple ships encountering simultaneously or ships with sharp turns), the model's performance may not be ideal, and it may be difficult to accurately identify real collision avoidance behavior.  
Future research may explore several promising directions. Given that AIS data exhibit local characteristics in different water areas, it is possible to conduct ship collision avoidance behavior identification in various complex water areas (such as narrow straits, port basins, etc.). This would allow for the investigation of the characteristics of ship collision avoidance behaviors in different water areas, as well as the impact of these areas on model identification. In addition, to enhance the reliability of data mining, multi-source data fusion can be employed by incorporating additional data types, such as radar data, video data, and satellite remote-sensing data. This approach would improve the model's ability to recognize complex encounter scenarios. Furthermore, for analyzing multiple turns in collision avoidance behavior, sequence modeling methods based on machine learning (such as LSTM networks) can be integrated to analyze trajectory changes, thereby further enhancing the accuracy of ship behavior identification. In practical applications, for the autonomous navigation of unmanned ships, combining this model with path-planning algorithms can lead to more intelligent and automated collision avoidance systems tailored for vessels of different scales. This integration would provide real-time decision making support for collision avoidance.  
In conclusion, the model proposed in this paper has significant application value in the intelligentization of waterway traffic and autonomous collision avoidance for unmanned ships. However, it still requires further optimization to adapt to more complex navigation environments and diverse ship behaviors. With future improvements, the model is expected to provide more reliable technical support for improving maritime traffic safety.