Air traffic control information extraction method based on pre-trained language models

Abstract

In air traffic management, the controller uses the control instruction to adjust the aircraft status, and the pilot confirms by repeating the control instruction. The correct understanding of control instruction is of great significance to flight safety. This paper proposes a new method of air traffic control information extraction, which is based on pre-training and fine tuning of the pre-training language model. It uses transfer learning to extract regulatory information under the condition of small samples. This method can not only reduce the cost of training data annotation, but also improve the accuracy of information extraction. The simulation results show that the accuracy of the new model is not less than 98%, and the key information in the control instructions can be extracted effectively. This method can improve the intelligence of air traffic control system, assist controllers to understand the contents of control instructions, support flight conflict detection, and ensure air transport safety.

Keywords

• air transportation|control instruction|information extraction|pre-trained language model|transfer learning