In recent years, the application prospect of urban logistics unmanned aerial vehicles has attracted extensive attention. The high-density operation of UAVs requires autonomous separation maintenance capability. To achieve autonomous separation maintenance, it is necessary to conduct autonomous track prediction and formulate the required separation accordingly. Based on the target level of safety requirements for UAV operation, aiming at the autonomous separation maintenance ability of UAVs and considering the accuracy of track prediction, a method to calculate the required separation between UAVs is proposed. This study consists of two parts. Firstly, based on historical data, the position prediction error of the flight track is investigated. Using a machine learning model, a two-stage track prediction method, which involves classification followed by prediction, is proposed for urban logistics UAV track data. Subsequently, based on the track prediction error distribution, by designing a gas model and a position error probability model, a separation-formulating model for urban logistics UAVs in free flight is proposed in which UAV maneuverability is considered. By applying this model, the required separation is formulated for UAVs. When the required separation is set to 48.5 m, the overall collision risk meets the TLS requirements. The research provides a feasible method for establishing autonomous separation for urban logistics UAVs.

This paper presents a method to calculate vertical overlap probability based on the position error caused by pilot control. Firstly, a fuzzy correlation model is established and combined with Quick Access Recorder (QAR) data. The correlation rules between QAR parameters and vertical position error are mined, and the relevance is calculated according to the rules. The results show that the correlation between the control column position and the vertical position error is the strongest. According to the flight dynamics principle, the Vertical Attitude Adjustment Model (VAAM) of an aircraft during the approach process is then established. The Pilot Operating Model (POM), which describes different actions taken by pilots when they encounter different position errors, is also created by employing the concepts of the Markov decision process. The VAAM can restore the process of changing the attitude of the aircraft by the pilot operating column and can simulate the position error of the aircraft in the approach process when combined with the POM. Based on the previous simulated result and considering the overlap condition of the Interval Management (IM) aircraft overlap, the vertical overlap process in the paired approach can then be simulated. We employ MATLAB to carry out numerous simulations. Changing the accuracy of pilot operation in the simulations show that the lower the accuracy, the greater the overlap probability and frequency. However, when the accuracy is between 61% and 73% (61%–73% is the range of operational accuracy based on QAR data analysis), the vertical overlap probability and frequency are maintained at about 5% and 20 flights per hour, respectively.

To achieve the forward movement of the aircraft landing attitude ultra-limit, this paper builds a deep learning-based aircraft landing attitude warning system. The early warning system includes four modules: data pretreatment, feature dimensionality reduction, prediction, and judgment. Subsequently, through data pretreatment methods such as data cleaning, frequency normalization, data standardization, and feature classification, the experimental dataset is transformed into a form recognizable by machine learning algorithms and neural network models. The necessary feature parameters are extracted to form a deep learning training dataset. Then, the Max-Relevance and Min-Redundancy algorithm was applied to screen the QAR (Quick Access Recorder) parameters with the highest correlation with the predictor variables, and the LSTM network model was established to predict the pitch and roll angles of the aircraft landing, respectively. Evaluation metrics are used to determine the optimal model parameters. Finally, the confusion matrix is introduced to test the prediction effect of the model, and through the secondary indicators of the confusion matrix, the prediction accuracy of the established landing attitude warning system is 94.83% for the pitch angle and 91.18% for the roll angle. It also provides pilots with a 5 s time margin to avoid risks. The system can effectively issue early warnings for ultra-limit landing attitude events and, based on the prediction results, identify the types of risks.

The paired approach can improve the efficiency of closely spaced parallel runways. Calculating the probability and frequency of horizontal overlap is an indispensable step when evaluating the horizontal collision risk of the paired approach. As the generation of horizontal overlap probability is closely related to horizontal position error, we propose a calculation method of horizontal overlap probability based on position error from the perspective of pilot operation. First, according to the principle of flight mechanics, the attitude adjustment model is established for the horizontal direction of the approach process, and the pilot’s operation model for various position errors is based on the concept of the stochastic process. This attitude adjustment model can replicates the process of the pilot operating the steering column to change the aircraft’s attitude. When combined with the pilot’s operation model, it is possible to simulate the position errors generated during the approach process. Building on this, the horizontal overlapping conditions of two aircraft are analyzed to simulate the horizontal overlap process in the paired approach. The duration and instances of overlap counted and the ratio between these results and the total running time give the overlap probability and frequency. Multiple simulations in MATLAB reveal that higher pilot operating accuracy shortens the time for the aircraft to align with the course, whereas lower accuracy leads to unstable horizontal position errors. Furthermore, the horizontal overlap in paired approaches primarily occurs at the beginning of the procedure, and enhancing the pilot’s operating accuracy does not significantly affect the probability and frequency of horizontal overlap.