# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 3 Results
### Comparative Analysis
Figure 14d presents the curvature calculation results under data fluctuation. Based on the algorithm's filtering criteria, continuous variations with fewer than six occurrences were excluded, thus eliminating wave and current interference. The turning points identified by the algorithm represent the vessel's actual collision avoidance maneuvers. Table 2 shows that both the proposed Curvature-Based Ship Collision Avoidance Behavior Recognition algorithm and the DP algorithm can identify that the ship has avoidance behavior. The DP algorithm can identify the general trend of the ship's trajectory and retains more key points as the threshold is reduced. However, as seen in the table,  
when the threshold is set to 0.001, the DP algorithm misses the earliest turning point, leading to the omission of important information. When the threshold is set to 0.0001, the DP algorithm fails to identify data disturbances and incorrectly classifies the disturbed data points as the earliest turning points. The Curvature-Based Ship Collision Avoidance Behavior Recognition algorithm proposed in this paper effectively avoids the impact of wind, waves, current, and transmission on trajectory data and accurately identifies the true collision avoidance turning points of the ship. Based on the identified collision avoidance turning points, the next step of the model is to identify ship encounter pairs and identify the scenario. By using the ship's position, speed, and course information at the collision avoidance turning points, the model calculates the DCPA and the TCPA and filters out ship encounter pairs. The results are shown in Table 3 and Figure 15.  
Table 3 shows the ship encounter pair identified by the model. In scenario 1, which is matched by trajectory 1, it can be seen that the DCPA is 1.31 nautical miles, which is less than 1.5 nautical miles, and the TCPA is 0.21 h, or 12.6 min, which is less than 30 min. In scenario 2, which is matched by trajectory 2, the DCPA is 1.45 nautical miles, and the TCPA is 0.173 h, or 10.39 min. In both scenarios, the ship encounter pairs have a collision risk. Next, based on the information of ship encounter pairs, the relative bearing and the course difference between the two vessels can be calculated. According to the encounter scenario classification standard, the encounter scenario is determined. With the vessel's heading adjusted to 000\({}^{\circ}\) (north), in scenario 1, the relative bearing is 5.03\({}^{\circ}\), which falls within the range of 000\({}^{\circ}\) to 006\({}^{\circ}\) or 354\({}^{\circ}\) to 360\({}^{\circ}\); the course difference is 184.7\({}^{\circ}\), which falls within the range of 170\({}^{\circ}\) to 190\({}^{\circ}\), meeting the criteria for a head-on scenario. In scenario 2, the relative bearing is 42.37\({}^{\circ}\), which falls within the range of 006\({}^{\circ}\) to 112.5\({}^{\circ}\); the course difference is 148\({}^{\circ}\), so scenario 2 is a crossing scenario. In conclusion, by using the collision avoidance behavior mining model considering encounter scenarios, it mines the collision avoidance \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **The Earliest Collision Avoidance** & **Turning Point** & \\
**Turning Point** & **Course (\({}^{\circ}\))** & **Timestamp (s)** \\ \hline \multirow{4}{*}{Trajectory 1} & Actual trajectory & 204.815909 & 1122 \\  & DP threshold 0.001 & 198.585579 & 1243 \\  & DP threshold 0.0001 & 206.121105 & 721 \\  & Curvature-Based Ship Collision Avoidance & 204.815909 & 1122 \\  & Behavior Recognition algorithm & & \\ \hline \multirow{4}{*}{Trajectory 2} & Actual trajectory & 308.555747 & 1902 \\  & DP threshold 0.001 & 297.603378 & 2042 \\ \cline{1-1}  & DP threshold 0.0001 & 307.380625 & 200 \\ \cline{1-1}  & Curvature-Based Ship Collision Avoidance & 308.555747 & 1902 \\ \cline{1-1}  & Behavior Recognition algorithm & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of ship collision avoidance turning point identification results.