## 6 Conclusions and Discussion  
Collision risk assessment between manned aircraft and drones is a key technology for the safe operation of air transport in low-altitude airspace. This paper begins with the problem of the illegal flight of drones, leading to the risk of collision with manned aircraft in the take-off and landing stages. A short-term risk assessment method between manned aircraft and drones has been implemented to identify the collision risk and enhance the safe operation of air traffic. The main findings of this paper are as follows:  
First, based on the historical ADS-B trajectory data of manned aircraft, a short-term trajectory prediction model of manned aircraft in different flight stages was conducted. The original manned aircraft trajectory data were collected. Data processing was performed, including the removal of noise and outliers, and the reconstruction of the trajectory into equally spaced data to improve data quality. The model comprehensively takes the longitude, latitude, altitude, speed, and other information of the manned aircraft as inputs in the trajectory prediction model to learn the flight intention of the manned aircraft. The performance of the model was compared with the commonly used trajectory partition models. Second, considering the flexible and changeable characteristics of drones, and based on the historical trajectory data, a QRDGRU structure is proposed to predict the trajectory of the drone. The model integrates the bidirectional gated recurrent unit structure and the quartile regression structure, which can obtain the position distribution of the drone. The proposed model is compared with traditional GRU, SVR, QRLSTM and QGRU models to testify its performance. The performance indicators confirm the superiority of the established model. Third, given the difficulty in estimating the uncertain collision risk between manned aircraft and drones, a Monte-Carlo-based collision probability estimation model is proposed. Based on the predicted manned aircraft trajectory and drone position distribution, the estimated probability is calculated by the Monte Carlo simulation method. Time to the closest distance at the encountering point, and the collision probability between the drone and the manned aircraft at the closest point are calculated. This paper predicts the conflict risk between non-cooperative drones and manned aircraft in the take-off and landing phases. The model structure can be directly applied by airport operators or air transport managers to identify the potential risk between manned aircraft and drones ahead of time. The intention of drones can be judged to detect dangerous behaviors. Furthermore, according to the estimated time to the closest distance at the encountering point, and the collision probability between manned aircraft and drones, different warning strategies can be implemented based on the predicted conflict levels. However, due to the limitation of acquired data, the following aspects can be further improved. First, for trajectory prediction of manned aircraft, only ADS-B data are currently considered, and there is no support for flight plans, high-altitude weather, and other data. The performance of the model can be further improved as long as these parameters can be obtained and input into the model. Second, the predicted trajectory of drones is only based on the historic ADS-B data, while the aircraft performance is not incorporated. As a matter of fact, the weather, especially wind, and the maneuverability of drones may influence the predicted position distribution. Future studies should also be conducted with regard to the intention of drones. Third, in the estimation of the collision probability between the drone and manned aircraft, since the collision between the drone and manned aircraft cannot be tested in actual situations, this paper translates the trajectory to generate an intersection. The behaviors of drones and manned aircraft may change for risk abatement in real conditions.