Semantic parsing technology of air traffic control instruction in fusion airspace based on deep learning

被引：0

作者：

Liu P. ^{[1
]}

Zhu X. ^{[1
]}

机构：

[1] AVIC Xi’an Flight Automatic Control Research Institute, Xi’an

来源：

Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2023年 / 44卷

关键词：

deep learning; fusion airspace; instruction parse; intention recognition; joint model; slot filling;

D O I：

10.7527/S1000-6893.2022.27592

中图分类号：

学科分类号：

摘要：

With the rapid development of UAV technology，the number of UAVs and airspace demand has increased significantly，which make it a trend for UAVs to enter integrated airspace. To modify the traditional ATC management pattern of“man in loop”and increase the control efficiency of fusion airspace，deep learning method is applied to air traffic control instruction parsing. According to the characteristics of ATC terms，this paper sorted，expanded，classified and annotated the instructions to write an ATC instructions dataset which can be used for learning of natural language understanding model. BiGRU-CRF model is used as the infrastructure of deep learning，and attention mechanism and intention feedback mechanism are added to construct a joint model to obtain instruction intention and instruction parameters. The evaluation results on ATC instruction dataset and ATIS dataset show that the model is nearly 1. 5% better than the infrastructure in the task of intent identification and slot filling. The method presented in this paper is practical and effective，which provides strong support for the development of UAV natural language control technology. © 2023 AAAS Press of Chinese Society of Aeronautics and Astronautics. All rights reserved.

引用

共 21 条

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