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Neurorobot."],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3389\/fnbot.2025.1482327","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T07:05:57Z","timestamp":1743577557000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Study on the standardization method of radiotelephony communication in low-altitude airspace based on BART"],"prefix":"10.3389","volume":"19","author":[{"given":"Weijun","family":"Pan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boyuan","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiyuan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,4,2]]},"reference":[{"key":"ref1","first-page":"1409","article-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau","year":"2014","journal-title":"arXiv"},{"key":"ref2","first-page":"1808.07561","article-title":"Training deeper neural machine translation models with transparent attention","author":"Bapna","year":"2018","journal-title":"arXiv"},{"key":"ref3","first-page":"1406","article-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation","author":"Cho","year":"2014","journal-title":"arXiv"},{"key":"ref4","year":"2015"},{"key":"ref5","volume-title":"2023 statistical bulletin on civil aviation transport airports in China","year":"2024"},{"key":"ref6","author":"Devlin","year":"2019"},{"key":"ref7","author":"Geac\u0103r","year":"2010"},{"key":"ref8","author":"Glaser-Opitz","year":"2015"},{"key":"ref9","year":"2020"},{"key":"ref10","first-page":"1910.13461","article-title":"Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension","author":"Lewis","year":"2019","journal-title":"arXiv"},{"key":"ref11","first-page":"74","article-title":"Rouge: a package for automatic evaluation of summaries","volume-title":"Text summarization branches out","author":"Lin","year":"2004"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"4572","DOI":"10.1109\/TITS.2019.2940992","article-title":"A real-time ATC safety monitoring framework using a deep learning approach","volume":"21","author":"Lin","year":"","journal-title":"IEEE Trans. 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