Ernie-Gram BiGRU Attention: An Improved Multi-Intention Recognition Model for Air Traffic Control

Pan, Weijun; Jiang, Peiyuan; Wang, Zhuang; Li, Yukun; Liao, Zhenlong

doi:10.3390/aerospace10040349

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Improving model generalization is a challenging task that requires extensive research. (3) Since control instructions are composed of a series of keywords (Pan et al, 2023 ), evaluating the generated pilot repetition instructions using ROUGE-N and ROUGE-L standards requires the segmentation of the control instructions. This necessitates the construction of a dictionary, adding extra workload.…”

Section: Challenges In Prg and Our Workmentioning

confidence: 99%

Research on automatic pilot repetition generation method based on deep reinforcement learning

Pan,

Jiang,

et al. 2023

Front. Neurorobot.

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Using computers to replace pilot seats in air traffic control (ATC) simulators is an effective way to improve controller training efficiency and reduce training costs. To achieve this, we propose a deep reinforcement learning model, RoBERTa-RL (RoBERTa with Reinforcement Learning), for generating pilot repetitions. RoBERTa-RL is based on the pre-trained language model RoBERTa and is optimized through transfer learning and reinforcement learning. Transfer learning is used to address the issue of scarce data in the ATC domain, while reinforcement learning algorithms are employed to optimize the RoBERTa model and overcome the limitations in model generalization caused by transfer learning. We selected a real-world area control dataset as the target task training and testing dataset, and a tower control dataset generated based on civil aviation radio land-air communication rules as the test dataset for evaluating model generalization. In terms of the ROUGE evaluation metrics, RoBERTa-RL achieved significant results on the area control dataset with ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.9962, 0.992, and 0.996, respectively. On the tower control dataset, the scores were 0.982, 0.954, and 0.982, respectively. To overcome the limitations of ROUGE in this field, we conducted a detailed evaluation of the proposed model architecture using keyword-based evaluation criteria for the generated repetition instructions. This evaluation criterion calculates various keyword-based metrics based on the segmented results of the repetition instruction text. In the keyword-based evaluation criteria, the constructed model achieved an overall accuracy of 98.8% on the area control dataset and 81.8% on the tower control dataset. In terms of generalization, RoBERTa-RL improved accuracy by 56% compared to the model before improvement and achieved a 47.5% improvement compared to various comparative models. These results indicate that employing reinforcement learning strategies to enhance deep learning algorithms can effectively mitigate the issue of poor generalization in text generation tasks, and this approach holds promise for future application in other related domains.

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Section: Challenges In Prg and Our Workmentioning

confidence: 99%

Research on automatic pilot repetition generation method based on deep reinforcement learning

Pan,

Jiang,

et al. 2023

Front. Neurorobot.

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“…In the field of Air Traffic Control (ATC), automated pilot agent systems represent a significant area of research. Air Traffic Control Officers (ATCOs) must complete simulated equipment training before obtaining qualifications to work in real ATC scenarios 4 , 5 . The training scenarios are depicted in Fig.…”

Section: Introductionmentioning

confidence: 99%

SLKIR: A framework for extracting key information from air traffic control instructions Using small sample learning

Jiang,

Zeng,

Pan

et al. 2024

Sci Rep

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In air traffic control (ATC), Key Information Recognition (KIR) of ATC instructions plays a pivotal role in automation. The field's specialized nature has led to a scarcity of related research and a gap with the industry's cutting-edge developments. Addressing this, an innovative end-to-end deep learning framework, Small Sample Learning for Key Information Recognition (SLKIR), is introduced for enhancing KIR in ATC instructions. SLKIR incorporates a novel Multi-Head Local Lexical Association Attention (MHLA) mechanism, specifically designed to enhance accuracy in identifying boundary words of key information by capturing their latent representations. Furthermore, the framework includes a task focused on prompt, aiming to bolster the semantic comprehension of ATC instructions within the core network. To overcome the challenges posed by category imbalance in boundary word and prompt discrimination tasks, tailored loss function optimization strategies are implemented, effectively expediting the learning process and boosting recognition accuracy. The framework's efficacy and adaptability are demonstrated through experiments on two distinct ATC instruction datasets. Notably, SLKIR outperforms the leading baseline model, W2NER, achieving a 3.65% increase in F1 score on the commercial flight dataset and a 12.8% increase on the training flight dataset. This study is the first of its kind to apply small-sample learning in KIR for ATC and the source code of SLKIR will be available at: https://github.com/PANPANKK/ATC_KIR.

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A data fusion method for maritime traffic surveillance: The fusion of AIS data and VHF speech information

Chen,

Qi,

Huang

et al. 2024

Ocean Engineering

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Ernie-Gram BiGRU Attention: An Improved Multi-Intention Recognition Model for Air Traffic Control

Cited by 5 publications

References 23 publications

Research on automatic pilot repetition generation method based on deep reinforcement learning

Research on automatic pilot repetition generation method based on deep reinforcement learning

SLKIR: A framework for extracting key information from air traffic control instructions Using small sample learning

A data fusion method for maritime traffic surveillance: The fusion of AIS data and VHF speech information

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