Beihang University

Biyue Li

Xianbin Cao

Wenbo Du

Yumeng Li

Xi Bo Zhu

Lei Zheng

This application provides an airspace complexity evaluation method based on deep unsupervised learning for air traffic management, which includes the following parts. A stacked autoencoder is used to establish an airspace complexity evaluation model. Input the airspace complexity factors into the stacked autoencoder to obtain the low-dimensional embedded representations of the airspace complexity factors. Cluster the low-dimensional embedded points to capture the centroids of the airspace complexity data. The application utilizes the soft assignment distribution and real assignment distribution of the embedded representations to construct a training loss function which optimizes the airspace complexity evaluation model by gradient descent algorithm. The trained airspace complexity evaluation model and the three obtained cluster centroids describing the airspace complexity level are used to evaluate the current airspace complexity.

Deep unsupervised learning approach, device and storage medium for airspace complexity evaluation

Provided are flight conflict resolution method and apparatus based on ultimatum game theory. The method includes: obtaining a first priority of a first aircraft and a second priority of a second aircraft when it is determined that a minimum distance between the first aircraft and the second aircraft within a preset time period is less than a preset distance; determining a first angle and a second angle of the first aircraft and a fourth angle and a fifth angle of the second aircraft according to the first priority, the second priority, and a preset limiting deflection angle; determining a third angle of the first aircraft and a sixth angle of the second aircraft; determining a first deflection angle of the first aircraft and a second deflection angle of the second aircraft according to the first and second priorities, the first, second, third, fourth, fifth and sixth angles.

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Beijing, China

Biyue Li