基于深度学习的融合空域空管指令语义解析技术

doi:10.7527/S1000-6893.2022.27592

Abstract

Abstract:

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.

Key words: fusion airspace, instruction parse, deep learning, intention recognition, slot filling, joint model

CLC Number:

V249.122⁺.9

Pengyu LIU, Xueyao ZHU. Semantic parsing technology of air traffic control instruction in fusion airspace based on deep learning[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(S1): 727592-727592.

Figures/Tables 8

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References 21

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指令	Maintain			FL		180	until	passing	XIY
语义槽	O			O		B-height_level	B-loc_limit	I-loc_limit	B-loc
意图	height
指令	taxi	to	gate	37	via	B3	A	A3
语义槽	O	O	O	B-gate_number	O	B-taxiway_number	I-taxiway_number	I-taxiway_number
意图	taxi
指令	contact			Shanghai		approach	on	118.6
语义槽	O			B-control_place		B-control_type	O	B-frequency
意图	frequency

方法	意图识别准确率/%	语义槽填充F₁值/%
BiGRU	93.41	91.24
BiGRU + CRF	93.33	91.79
BiGRU + Attention	94.40	92.18
BiGRU + Attention + CRF	94.28	92.66
本文联合模型	95.14	92.70

方法	意图识别准确率/%	语义槽填充F₁值/%
BiGRU	95.21	95.02
BiGRU + CRF	95.15	95.27
BiGRU + Attention	95.81	95.36
BiGRU + Attention + CRF	95.62	95.55
本文联合模型	96.13	95.56

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

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