To mitigate the problem of multiple unmanned aircraft systems (MUAS) conflicts at low altitude and ensure the operational safety, this paper proposes a Multivariate Combined Conflict Detection (MCCD) method for MUAS by combining the characteristics of nominal and probabilistic trajectory method. Firstly, the structural framework of the MCCD method is established based on the concept of potential conflict pool, and a detection pattern is derived for MUAS. Secondly, a three-dimensional conflict fast detection model is constructed by velocity obstacle methods, which can rapidly detect potential conflict risks. Thirdly, a trajectory prediction model is constructed by using bidirectional long-short term memory (Bi-LSTM) network, and then a probabilitybased conflict detection model can be obtained by the expected value and error distribution of trajectory prediction, which can accurately calculate the collision probability of UAS pair. By fully integrating the above models, the fast and accurate detection of MUAS conflicts is achieved. Finally, multiple conflicting trajectories are constructed to analyze the effectiveness of MCCD method, the tests indicate that the average detection time of the proposed method is less than 15ms, the false positive rate is less than 0.01 and the false negative rate is less than 0.0035. The results show that the MCCD has the accuracy advantage and better real-time performance for MUAS conflict detection compared to the method of velocity trend extrapolation, single probabilistic conflict detection and probabilistic neural network.INDEX TERMS conflict detection, multi-unmanned aircraft systems, potential conflict, probability estimation.

A bi-level optimization model for the logistics UAV air route network capacity evaluation based on traffic flow allocation is designed in order to meet the future trend of large-scale and normalized operation of logistics UAVs. The maximum sorties of logistics UAVs that can be served by the air route network are the upper-bound model objective, namely, the maximum flow of the logistics UAV air route network. The impedance function is constructed by considering safety and efficiency factors, and the lowerbound model objective function with the minimum logistics UAV air route network impedance value. An improved particle swarm optimization(PSO) algorithm is combined with the method of the successive algorithm(MSA) for solving the bi-level optimization model. To verify the effectiveness of the proposed model and algorithm, a simplified logistics UAV air route network is built. The results show that the proposed algorithm obtains reliable results after 26 iterations, and most segments capacity utilization rate is more than 70%. Parametric analysis of safe separation and algorithm population size shows that the capacity of logistics UAV air route network decreases with the increase of safe separation and the decreasing trend is gradually slowed down, and the optimal algorithm population size corresponding to different safe separations also varies. Based on the study described above, a logistics UAV air route network based on actual geographic information data is constructed, and the experimental results demonstrate that the suggested technique could be used to a specific scale of logistics UAV route network capacity evaluation and had validity.INDEX TERMS air traffic management, urban air mobility, UAV logistics, airspace capacity.

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