Study on the standardization method of radiotelephony communication in low-altitude airspace based on BART
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...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Neurorobotics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2025.1482327/full |
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| author | Weijun Pan Boyuan Han Peiyuan Jiang |
| author_facet | Weijun Pan Boyuan Han Peiyuan Jiang |
| author_sort | Weijun Pan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dbd32f1c5e8d4bd3a1d4a4eb3fd815cb |
| institution | OA Journals |
| issn | 1662-5218 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj-art-dbd32f1c5e8d4bd3a1d4a4eb3fd815cb2025-08-20T01:55:37ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182025-04-011910.3389/fnbot.2025.14823271482327Study on the standardization method of radiotelephony communication in low-altitude airspace based on BARTWeijun Pan0Boyuan Han1Peiyuan Jiang2Civil Aviation Flight University of China, Chengdu, ChinaCivil Aviation Flight University of China, Chengdu, ChinaUniversity of Electronic Science and Technology of China, Chengdu, ChinaThe 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.https://www.frontiersin.org/articles/10.3389/fnbot.2025.1482327/fullradiotelephony communicationair traffic controlBART modellow-altitude airspacedeep reinforcement learning |
| spellingShingle | Weijun Pan Boyuan Han Peiyuan Jiang Study on the standardization method of radiotelephony communication in low-altitude airspace based on BART Frontiers in Neurorobotics radiotelephony communication air traffic control BART model low-altitude airspace deep reinforcement learning |
| title | Study on the standardization method of radiotelephony communication in low-altitude airspace based on BART |
| title_full | Study on the standardization method of radiotelephony communication in low-altitude airspace based on BART |
| title_fullStr | Study on the standardization method of radiotelephony communication in low-altitude airspace based on BART |
| title_full_unstemmed | Study on the standardization method of radiotelephony communication in low-altitude airspace based on BART |
| title_short | Study on the standardization method of radiotelephony communication in low-altitude airspace based on BART |
| title_sort | study on the standardization method of radiotelephony communication in low altitude airspace based on bart |
| topic | radiotelephony communication air traffic control BART model low-altitude airspace deep reinforcement learning |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2025.1482327/full |
| work_keys_str_mv | AT weijunpan studyonthestandardizationmethodofradiotelephonycommunicationinlowaltitudeairspacebasedonbart AT boyuanhan studyonthestandardizationmethodofradiotelephonycommunicationinlowaltitudeairspacebasedonbart AT peiyuanjiang studyonthestandardizationmethodofradiotelephonycommunicationinlowaltitudeairspacebasedonbart |