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

## TL;DR

This paper introduces a new AI model to improve communication standardization in low-altitude airspace for better air traffic control.

## Contribution

The novel BART-Reinforcement Learning (BRL) model improves standardization of radiotelephony communication in ATC.

## Key findings

- BRL achieved a 10.5% improvement in accuracy on highly non-standardized training data.
- The model outperformed baseline models in standardizing various types of ATC instructions.
- Results show reinforcement learning can enhance automation and safety in low-altitude airspace.

## 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.