## 5 Conflict Risk Assessment between Drones and Manned Aircraft
Based on the trajectory data of the take-off stage and the landing stage of the manned aircraft and the collected drone trajectory data, the drone and the manned aircraft are placed in the same space by means of translation, and simulation experiments are carried out within four conflict risk levels. Figure 6 illustrates the conflict scenario based on the predicted drone and aircraft trajectory. The cylinder is the protected area of the manned aircraft when they meet at the nearest point; the solid line is the historical trajectory of the drone; the dotted line is the historical trajectory of the manned aircraft; the asterisk * is the predicted position of  
the manned aircraft when it meets at the nearest point. The continuous curve predicts the location distribution for the drone. It can be seen that most of the predicted drone trajectory points fall within the manned aircraft protection zone when arriving at the closest encountering point. The closest distance at the encountering point is 457.27 m. According to the current historical trajectory of the drone and the manned aircraft, it is predicted that the closest encountering point between the drone and the manned aircraft will be 18 s later. With Formula (8), it is estimated that the collision probability between the drone and the manned aircraft at the closest point is 81.63%. The air transport management department can make decisions based on the above simulation results. The Monte Carlo simulation tests were conducted at each flight level during the departure and arrival stages. The prediction accuracy of collision probability at different flight stages and levels can be obtained, as shown in Table 7. With the increase of altitude, the prediction accuracy of collision probability decreases. This is because the higher the altitude, the higher the flight speed. The mean absolute error for the trajectory prediction of the manned aircraft will be reduced as the altitude increases, which affects the collision probability prediction accuracy. Naturally, there is a tradeoff between correct detections and false alarms. For example, with a larger protection zone of the manned aircraft, the correct detection rate tends to be higher while the false alarm rate also tends to be higher. The performance indicators are also affected by the type of intruder. For example, it has been demonstrated in previous research that when the correct detection rate is around 80%, the false alarm rate is around 8% for \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Altitude (m)** & \multicolumn{2}{c}{**The Take-Off Stage**} & \multicolumn{2}{c}{**The Landing Stage**} \\ \hline  & \(Per_{cd}\) & \(Per_{fi}\) & \(Per_{cd}\) & \(Per_{fi}\) \\ \hline
0–100 & 97.37\% & 8.20\% & 96.97\% & 5.90\% \\
100–200 & 91.07\% & 11.63\% & 94.44\% & 12.70\% \\
200–300 & 88.89\% & 6.70\% & 90.00\% & 16.32\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Conflict prediction accuracy under different flight levels. Figure 6: A typical conflict scenario based on the predicted drone and aircraft trajectory. Zagi size as the intruder, 35% for ScanEagle size, and 90% for Cessna size as intruder [33]. In another study, alert results from 10,000 simulated unmanned aerial system tracks indicate that, to obtain a relatively high failure rate, the false alarm rate ranges from 18.89% to 71.38% based on different methods [27]. Overall, the correct detection rate ranges from 88.89% to 97.37% and the false alarm rate ranges from 5.9% to 16.32% in the current research. The model estimation results are generally comparable with those in previous research.