A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers

Zuluaga-Gómez, Juan; Prasad, Amrutha; Nigmatulina, Iuliia; Motlíček, Petr; Kleinert, Matthias

doi:10.3390/aerospace10050490

Cited by 13 publications

(6 citation statements)

References 79 publications

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“…The virtual simulation-pilot engine that uses AI tools to accelerate air traffic controller training by recognizing, understanding, and responding to spoken communications from trainees is introduced in [48]. The system incorporates state-of-the-art AI models and can be enhanced with real-time data or deliberate errors, achieving impressive accuracy rates in word recognition and callsign detection.…”

Section: Ai In Aviation Educationmentioning

confidence: 99%

Artificial Intelligence in Aviation: New Professionals for New Technologies

Kabashkin,

Misnevs,

Zervina

2023

Applied Sciences

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Major aviation organizations have highlighted the need to adopt artificial intelligence (AI) to transform operations and improve efficiency and safety. However, the aviation industry requires qualified graduates with relevant AI competencies to meet this demand. This study analyzed aviation engineering bachelor’s programs at European universities to determine if they are preparing students for AI integration in aviation by incorporating AI-related topics. The analysis focused on program descriptions and syllabi using semantic annotation. The results showed a limited focus on AI and machine learning competencies, with more emphasis on foundational digital skills. Reasons include the newness of aviation AI, its specialized nature, and implementation challenges. As the industry evolves, dedicated AI programs may emerge. But currently, curricula appear misaligned with stated industry goals for AI adoption. The study provides an analytical methodology and competency framework to help educators address this gap. Producing graduates equipped with AI literacy and collaboration skills will be key to aviation’s intelligent future.

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Section: Ai In Aviation Educationmentioning

confidence: 99%

Artificial Intelligence in Aviation: New Professionals for New Technologies

Kabashkin,

Misnevs,

Zervina

2023

Applied Sciences

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“…Our previous research on identifying speaker roles [4] mainly focused on a grammar-based bag-of-words system that was capable of performing speaker role identification with precision/recall values of 0.82/0.81 for ATCos and 0.84/0.85 for pilots, respectively. Also, in [28][29][30], we explored speaker change detection for ATC text. In [31], the authors mentioned that manually annotating pilot recordings was more challenging than annotating ATCo recordings due to their quality, speech rate, speaker accent, etc.…”

Section: Speaker Role Classificationmentioning

confidence: 99%

An Automatic Speaker Clustering Pipeline for the Air Traffic Communication Domain

Khalil,

Prasad,

Motlicek

et al. 2023

Aerospace

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In air traffic management (ATM), voice communications are critical for ensuring the safe and efficient operation of aircraft. The pertinent voice communications—air traffic controller (ATCo) and pilot—are usually transmitted in a single channel, which poses a challenge when developing automatic systems for air traffic management. Speaker clustering is one of the challenges when applying speech processing algorithms to identify and group the same speaker among different speakers. We propose a pipeline that deploys (i) speech activity detection (SAD) to identify speech segments, (ii) an automatic speech recognition system to generate the text for audio segments, (iii) text-based speaker role classification to detect the role of the speaker—ATCo or pilot in our case—and (iv) unsupervised speaker clustering to create a cluster of each individual pilot speaker from the obtained speech utterances. The speech segments obtained by SAD are input into an automatic speech recognition (ASR) engine to generate the automatic English transcripts. The speaker role classification system takes the transcript as input and uses it to determine whether the speech was from the ATCo or the pilot. As the main goal of this project is to group the speakers in pilot communication, only pilot data acquired from the classification system is employed. We present a method for separating the speech parts of pilots into different clusters based on the speaker’s voice using agglomerative hierarchical clustering (AHC). The performance of the speaker role classification and speaker clustering is evaluated on two publicly available datasets: the ATCO2 corpus and the Linguistic Data Consortium Air Traffic Control Corpus (LDC-ATCC). Since the pilots’ real identities are unknown, the ground truth is generated based on logical hypotheses regarding the creation of each dataset, timing information, and the information extracted from associated callsigns. In the case of speaker clustering, the proposed algorithm achieves an accuracy of 70% on the LDC-ATCC dataset and 50% on the more noisy ATCO2 dataset.

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“…This network effectively converts ATC voice instructions into machine-understandable control intentions and instruction parameters 12 . Zuluaga-Gomez J and others built an advanced entity parsing system in the intelligent pilot architecture by fine-tuning pre-trained language models (LM) 13 and achieved commendable results on the ATCO2 dataset 14 .…”

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 Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers

Cited by 13 publications

References 79 publications

Artificial Intelligence in Aviation: New Professionals for New Technologies

Artificial Intelligence in Aviation: New Professionals for New Technologies

An Automatic Speaker Clustering Pipeline for the Air Traffic Communication Domain

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

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