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Aerospace
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Controlling Speech Understanding and Air Traffic Safety Enhancement Based on...
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Controlling Speech Understanding and Air Traffic Safety Enhancement Based on AI

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Air Traffic and Transportation".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 26751

Special Issue Editors

Prof. Dr. Yi Lin

E-Mail Website
Guest Editor
College of Computer Science, Sichuan University, Chengdu 61000, China
Interests: deep learning; intelligent transportation system; machine learning; speech recognition; air traffic control
Special Issues, Collections and Topics in MDPI journals
Dr. Alexei Sharpanskykh

E-Mail Website
Guest Editor
Aerospace Engineering Department, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands
Interests: artificial intelligence techniques for air transport; multiagent systems; complex sociotechnical systems; distributed planning and scheduling; airports and airlines; urban air mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • air traffic control
  • artificial intelligence
  • language understanding
  • air traffic safety

Published Papers (6 papers)

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Research

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19 pages, 4671 KiB  
Article
A Counterfactual Framework Based on the Machine Learning Method and Its Application to Measure the Impact of COVID-19 Local Outbreaks on the Chinese Aviation Market
by Linfeng Zhang, Hongwu Tang and Lei Bian
Aerospace 2022, 9(5), 250; https://doi.org/10.3390/aerospace9050250 - 04 May 2022
Viewed by 1963
Abstract
COVID-19 affects aviation around the world. China’s civil aviation almost recovered to its pre-epidemic levels in the domestic market, but there are still local outbreaks that affect air traffic. This paper proposes measuring the impact of local outbreaks of COVID-19 by the machine [...] Read more.
COVID-19 affects aviation around the world. China’s civil aviation almost recovered to its pre-epidemic levels in the domestic market, but there are still local outbreaks that affect air traffic. This paper proposes measuring the impact of local outbreaks of COVID-19 by the machine learning method and the synthetic control method as a counterfactual control group to measure such an impact. In this study, we use the LightGBM algorithm to construct a counterfactual control group and transform the prediction problem from time series to the fitting problem at the spatial level. We find that machine learning methods can measure such an impact more accurately. We take local outbreaks in Beijing and Dalian as examples, and our measure of their impacts shows that the impact of an outbreak on intercity air traffic can be divided into lag, decline, stable, and recovery periods, and will last for a long period (more than 40 days) unless there are external stimuli, such as legal holidays. The outbreaks reduced the number of passengers in the cities by 90%. Finally, we show the impact on the air traffic network, and find that when a local outbreak happens in a big city, tourist cities or small stations will be greatly affected. Full article
(This article belongs to the Special Issue Controlling Speech Understanding and Air Traffic Safety Enhancement Based on AI)
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15 pages, 1042 KiB  
Article
A Deep Learning Approach for Short-Term Airport Traffic Flow Prediction
by Zhen Yan, Hongyu Yang, Fan Li and Yi Lin
Aerospace 2022, 9(1), 11; https://doi.org/10.3390/aerospace9010011 - 24 Dec 2021
Cited by 13 | Viewed by 3753
Abstract
Airport traffic flow prediction is a fundamental research topic in the field of air traffic flow management. Most existing works focus on the single airport traffic flow prediction with temporal dynamics but fail to consider the influence of the topological airport network. In [...] Read more.
Airport traffic flow prediction is a fundamental research topic in the field of air traffic flow management. Most existing works focus on the single airport traffic flow prediction with temporal dynamics but fail to consider the influence of the topological airport network. In this paper, a novel deep learning-based framework, called airport traffic flow prediction network (ATFPNet), is proposed to capture spatial-temporal dependencies of the historical airport traffic flow (departure and arrival) for the multiple-step situational (network-level) arrival flow prediction. Firstly, considering the nature of the airport distribution and the context of air transportation, a special semantic graph built on the flight schedule is applied to represent the airport network, which is the key to encoding the situational airport traffic flow into a single representation. Then, the graph convolution operator and the gated recurrent unit are combined to extract high-level transition patterns of airport traffic flow in the spatial and temporal dimensions. Finally, a real-world airport traffic flow dataset is applied to validate the effectiveness of the proposed model, and the experimental results demonstrate that the ATFPNet outperforms other baselines on different prediction horizons. Specifically, the proposed method achieves up to 17% MAE improvement compared to baselines. Based on the proposed approach, efficient traffic planning is expected to be achieved for airport management. Full article
(This article belongs to the Special Issue Controlling Speech Understanding and Air Traffic Safety Enhancement Based on AI)
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18 pages, 5724 KiB  
Article
Controller Fatigue State Detection Based on ES-DFNN
by Haijun Liang, Changyan Liu, Kuanming Chen, Jianguo Kong, Qicong Han and Tiantian Zhao
Aerospace 2021, 8(12), 383; https://doi.org/10.3390/aerospace8120383 - 07 Dec 2021
Cited by 4 | Viewed by 2583
Abstract
The fatiguing work of air traffic controllers inevitably threatens air traffic safety. Determining whether eyes are in an open or closed state is currently the main method for detecting fatigue in air traffic controllers. Here, an eye state recognition model based on deep-fusion [...] Read more.
The fatiguing work of air traffic controllers inevitably threatens air traffic safety. Determining whether eyes are in an open or closed state is currently the main method for detecting fatigue in air traffic controllers. Here, an eye state recognition model based on deep-fusion neural networks is proposed for determination of the fatigue state of controllers. This method uses transfer learning strategies to pre-train deep neural networks and deep convolutional neural networks and performs network fusion at the decision-making layer. The fused network demonstrated an improved ability to classify the target domain dataset. First, a deep-cascaded neural network algorithm was used to realize face detection and eye positioning. Second, according to the eye selection mechanism, the pictures of the eyes to be tested were cropped and passed into the deep-fusion neural network to determine the eye state. Finally, the PERCLOS indicator was combined to detect the fatigue state of the controller. On the ZJU, CEW and ATCE datasets, the accuracy, F1 score and AUC values of different networks were compared, and, on the ZJU and CEW datasets, the recognition accuracy and AUC values among different methods were evaluated based on a comparative experiment. The experimental results show that the deep-fusion neural network model demonstrated better performance than the other assessed network models. When applied to the controller eye dataset, the recognition accuracy was 98.44%, and the recognition accuracy for the test video was 97.30%. Full article
(This article belongs to the Special Issue Controlling Speech Understanding and Air Traffic Safety Enhancement Based on AI)
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13 pages, 432 KiB  
Article
A Context-Aware Language Model to Improve the Speech Recognition in Air Traffic Control
by Dongyue Guo, Zichen Zhang, Peng Fan, Jianwei Zhang and Bo Yang
Aerospace 2021, 8(11), 348; https://doi.org/10.3390/aerospace8110348 - 16 Nov 2021
Cited by 8 | Viewed by 2667
Abstract
Recognizing isolated digits of the flight callsign is an important and challenging task for automatic speech recognition (ASR) in air traffic control (ATC). Fortunately, the flight callsign is a kind of prior ATC knowledge and is available from dynamic contextual information. In this [...] Read more.
Recognizing isolated digits of the flight callsign is an important and challenging task for automatic speech recognition (ASR) in air traffic control (ATC). Fortunately, the flight callsign is a kind of prior ATC knowledge and is available from dynamic contextual information. In this work, we attempt to utilize this prior knowledge to improve the performance of the callsign identification by integrating it into the language model (LM). The proposed approach is named context-aware language model (CALM), which can be applied for both the ASR decoding and rescoring phase. The proposed model is implemented with an encoder–decoder architecture, in which an extra context encoder is proposed to consider the contextual information. A shared embedding layer is designed to capture the correlations between the ASR text and contextual information. The context attention is introduced to learn discriminative representations to support the decoder module. Finally, the proposed approach is validated with an end-to-end ASR model on a multilingual real-world corpus (ATCSpeech). Experimental results demonstrate that the proposed CALM outperforms other baselines for both the ASR and callsign identification task, and can be practically migrated to a real-time environment. Full article
(This article belongs to the Special Issue Controlling Speech Understanding and Air Traffic Safety Enhancement Based on AI)
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12 pages, 8881 KiB  
Article
Remote Sensing Image Super-Resolution for the Visual System of a Flight Simulator: Dataset and Baseline
by Wenyi Ge, Zhitao Wang, Guigui Wang, Shihan Tan and Jianwei Zhang
Aerospace 2021, 8(3), 76; https://doi.org/10.3390/aerospace8030076 - 15 Mar 2021
Cited by 1 | Viewed by 2418
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 [...] Read more.
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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23 pages, 3687 KiB  
Review
Spoken Instruction Understanding in Air Traffic Control: Challenge, Technique, and Application
by Yi Lin
Aerospace 2021, 8(3), 65; https://doi.org/10.3390/aerospace8030065 - 05 Mar 2021
Cited by 48 | Viewed by 10345
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. [...] Read more.
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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