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Dear colleagues,

It is well known that safety is always a popular research topic in the field of air traffic control (ATC), and any effort to improve the safety of ATC, from various aspects, deserves support. In current ATC procedures, speech communication with radio transmission is the primary way to exchange information between the controller and aircrew, in which a wealth of contextual situational dynamics are implicitly embedded. However, speech communication is also a typical human-in-the-loop (HITL) procedure in ATC, in which any speech error may cause communication misunderstanding between the controller and aircrew. Since communication is the first step of performing an instruction, a misunderstanding likely results in incorrect aircraft motion states and further potential conflict (safety risk) in air traffic safety. Thus, it is clear that understanding spoken language in air traffic control is particularly significant to ATC research. The main purpose of this research is to detect the communication errors that may cause potential safety risks, the implementation of which is capable of providing reliable warnings before the pilot performs the incorrect instruction. In addition, other techniques are considered to improve the air traffic safety, from the air traffic controller training, automatic planning, etc. Fortunately, thanks to the large amount of available industrial data storage and widespread applications of information technology, it is possible to obtain extra real-time traffic information from the speech communication, and further make contributions to the air traffic operation. This Special Issue focuses on applying the machine learning or artificial intelligence approaches to the research topics related to the air traffic safety, including but not limited the following items:

1) speech recognition for air traffic controlling speech;

2) language processing of air traffic instructions;

3) air traffic safety enhancement: system, techniques or case studies;

4) conflict detection and trajectory processing;

5) automatic decision, such as reinforcement learning;

6) improve air traffic safety from the air traffic controller training and simulator;

7) other air traffic and machine learning related research topics.

We sincerely invite participants with expertise in air traffic and computer science to contribute their paper to this Special Issue and share academic and industrial experience with the community. Let's work together to make further contributions to improve the safety of air traffic.

Dr. Yi Lin
Dr. Alexei Sharpanskykh
Guest Editors

Manuscript Submission Information

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Keywords

air traffic control
artificial intelligence
language understanding
air traffic safety

Published Papers (2 papers)

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Research

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Open AccessArticle

Remote Sensing Image Super-Resolution for the Visual System of a Flight Simulator: Dataset and Baseline

and

Aerospace 2021, 8(3), 76; https://doi.org/10.3390/aerospace8030076 - 15 Mar 2021

Viewed by 664

Abstract

High-resolution remote sensing images are the key data source for the visual system of a flight simulator for training a qualified pilot. However, due to hardware limitations, it is an expensive task to collect spectral and spatial images at very high resolutions. In this work, we try to tackle this issue with another perspective based on image super-resolution (SR) technology. First, we present a new ultra-high-resolution remote sensing image dataset named Airport80, which is captured from the airspace near various airports. Second, a deep learning baseline is proposed by applying the generative and adversarial mechanism, which is able to reconstruct a high-resolution image during a single image super-resolution. Experimental results for our benchmark demonstrate the effectiveness of the proposed network and show it has reached satisfactory performances. Full article

(This article belongs to the Special Issue Controlling Speech Understanding and Air Traffic Safety Enhancement Based on AI)

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Review

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Open AccessReview

Spoken Instruction Understanding in Air Traffic Control: Challenge, Technique, and Application

Yi Lin

Aerospace 2021, 8(3), 65; https://doi.org/10.3390/aerospace8030065 - 05 Mar 2021

Cited by 3 | Viewed by 793

Abstract

In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and aircrew. A wealth of contextual situational dynamics is embedded implicitly; thus, understanding the spoken instruction is particularly significant to the ATC research. In this paper, a comprehensive review related to spoken instruction understanding (SIU) in the ATC domain is provided from the perspective of the challenges, techniques, and applications. Firstly, a full pipeline is represented to achieve the SIU task, including automatic speech recognition, language understanding, and voiceprint recognition. A total of 10 technique challenges are analyzed based on the ATC task specificities. In succession, the common techniques for SIU tasks are categorized from common applications, and extensive works in the ATC domain are also reviewed. Finally, a series of future research topics are also prospected based on the corresponding challenges. The author sincerely hopes that this work is able to provide a clear technical roadmap for the SIU tasks in the ATC domain and further make contributions to the research community. Full article

(This article belongs to the Special Issue Controlling Speech Understanding and Air Traffic Safety Enhancement Based on AI)

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