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. 2025 Apr 2:19:1482327.
doi: 10.3389/fnbot.2025.1482327. eCollection 2025.

Study on the standardization method of radiotelephony communication in low-altitude airspace based on BART

Weijun Pan  1 , Boyuan Han  1 , Peiyuan Jiang  2
Affiliations

Study on the standardization method of radiotelephony communication in low-altitude airspace based on BART

Weijun Pan et al. Front Neurorobot. .

Abstract

The development of air traffic control (ATC) automation has been constrained by the scarcity and low quality of communication data, particularly in low-altitude complex airspace, where non-standardized instructions frequently hinder training efficiency and operational safety. This paper proposes the BART-Reinforcement Learning (BRL) model, a deep reinforcement learning model based on the BART pre-trained language model, optimized through transfer learning and reinforcement learning techniques. The model was evaluated on multiple ATC datasets, including training flight data, civil aviation operational data, and standardized datasets generated from Radiotelephony Communications for Air Traffic Services. Evaluation metrics included ROUGE and semantic intent-based indicators, with comparative analysis against several baseline models. Experimental results demonstrate that BRL achieves a 10.5% improvement in overall accuracy on the training dataset with the highest degree of non-standardization, significantly outperforming the baseline models. Furthermore, comprehensive evaluations validate the model's effectiveness in standardizing various types of instructions. The findings suggest that reinforcement learning-based approaches have the potential to significantly enhance ATC automation, reducing communication inconsistencies, and improving training efficiency and operational safety. Future research may further optimize standardization by incorporating additional contextual factors into the model.

Keywords: BART model; air traffic control; deep reinforcement learning; low-altitude airspace; radiotelephony communication.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Deep reinforcement learning model architecture.
Figure 2
Figure 2
The distribution of word vectors for generated text.
Figure 3
Figure 3
The distribution of word vectors for training text.
Figure 4
Figure 4
The distribution of word vectors for augmented operational text.
Figure 5
Figure 5
The distribution of word vectors for operational text.
Figure 6
Figure 6
Distribution of intent in training texts.
See this image and copyright information in PMC

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