Detail publikace

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

ZULUAGA-GOMEZ, J. PRASAD, A. NIGMATULINA, I. MOTLÍČEK, P. KLEINERT, M.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

In this paper we propose a novel virtual simulation-pilot engine for speeding up air traffic controller (ATCo) training by integrating different state-of-the-art artificial intelligence (AI)-based tools. The virtual simulation-pilot engine receives spoken communications from ATCo trainees, and it performs automatic speech recognition and understanding. Thus, it goes beyond only transcribing the communication and can also understand its meaning. The output is subsequently sent to a response generator system, which resembles the spoken read-back that pilots give to the ATCo trainees. The overall pipeline is composed of the following submodules: (i) an automatic speech recognition (ASR) system that transforms audio into a sequence of words; (ii) a high-level air traffic control (ATC)-related entity parser that understands the transcribed voice communication; and (iii) a text-to-speech submodule that generates a spoken utterance that resembles a pilot based on the situation of the dialogue. Our system employs state-of-the-art AI-based tools such as Wav2Vec 2.0, Conformer, BERT and Tacotron models. To the best of our knowledge, this is the first work fully based on open-source ATC resources and AI tools. In addition, we develop a robust and modular system with optional submodules that can enhance the system's performance by incorporating real-time surveillance data, metadata related to exercises (such as sectors or runways), or even a deliberate read-back error to train ATCo trainees to identify them. Our ASR system can reach as low as 5.5% and 15.9% absolute word error rates (WER) on high- and low-quality ATC audio. We also demonstrate that adding surveillance data into the ASR can yield a callsign detection accuracy of more than 96%.

Klíčová slova

air traffic controller training; simulation-pilot agent; BERT; automatic speech recognition and understanding; speech synthesis

Autoři

ZULUAGA-GOMEZ, J.; PRASAD, A.; NIGMATULINA, I.; MOTLÍČEK, P.; KLEINERT, M.;

Vydáno

22. 5. 2023

Nakladatel

MDPI

Místo

BASEL

ISSN

2226-4310

Periodikum

Aerospace

Ročník

10

Číslo

5

Stát

Švýcarská konfederace

Strany od

1

Strany do

25

Strany počet

25

URL

https://www.mdpi.com/2226-4310/10/5/490

Plný text v Digitální knihovně

http://hdl.handle.net/11012/244949

BibTex

@article{BUT187716,
  author="Juan {Zuluaga-Gomez} and Amrutha {Prasad} and Iuliia {Nigmatulina} and Petr {Motlíček} and Matthias {Kleinert}",
  title="A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers",
  journal="Aerospace",
  year="2023",
  volume="10",
  number="5",
  pages="1--25",
  doi="10.3390/aerospace10050490",
  issn="2226-4310",
  url="https://www.mdpi.com/2226-4310/10/5/490"
}