Prediction and extraction of tower controller commands for speech recognition applications

Ohneiser, Oliver; Helmke, Hartmut; Shetty, Shruthi; Kleinert, Matthias; Ehr, Heiko; Murauskas, Šarūnas; Pagirys, Tomas

doi:10.1016/j.jairtraman.2021.102089

Cited by 14 publications

(6 citation statements)

References 3 publications

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“…For example, 144 heading commands are modeled as being usually possible with the qualifiers RIGHT and LEFT for the value range from 005, 010 to 355, 360. For the multiple remote tower environment, a context portion predicted of below 10% was achieved [25].…”

Section: Related Work On Automatic Speech Recognition (Asr)mentioning

confidence: 98%

“…The most advanced command prediction techniques base on machine learning and cover all relevant flight phases in the approach, en-route, and tower environment [22][23][24]. The command prediction error rate of an early implementation for multiple remote tower simulation command predictions was below 10% [25]. An ATC command prediction error rate of even 0.3% has been achieved for simulated Prague approach environment [26].…”

Section: Related Work On Automatic Speech Recognition (Asr)mentioning

confidence: 99%

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Integrating Eye- and Mouse-Tracking with Assistant Based Speech Recognition for Interaction at Controller Working Positions

2021

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Assistant based speech recognition (ABSR) prototypes for air traffic controllers have demonstrated to reduce controller workload and aircraft flight times as a result. However, two aspects of ABSR could enhance benefits, i.e., (1) the predicted controller commands that speech recognition engines use can be more accurate, and (2) the confirmation process of ABSR recognition output, such as callsigns, command types, and values by the controller, can be less intrusive. Both tasks can be supported by unobtrusive eye- and mouse-tracking when using operators’ gaze and interaction data. First, probabilities for predicted commands should consider controllers’ visual focus on the situation data display. Controllers will more likely give commands to aircraft that they focus on or where there was a mouse interaction on the display. Furthermore, they will more likely give certain command types depending on the characteristics of multiple aircraft being scanned. Second, it can be determined via eye-tracking instead of additional mouse clicks if the displayed ABSR output has been checked by the controller and remains uncorrected for a certain amount of time. Then, the output is assumed to be correct and is usable by other air traffic control systems, e.g., short-term conflict alert. If the ABSR output remains unchecked, an attention guidance functionality triggers different escalation levels to display visual cues. In a one-shot experimental case study with two controllers for the two implemented techniques, (1) command prediction probabilities improved by a factor of four, (2) prediction error rates based on an accuracy metric for three most-probable aircraft decreased by a factor of 25 when combining eye- and mouse-tracking data, and (3) visual confirmation of ABSR output promises to be an alternative for manual confirmation.

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Section: Related Work On Automatic Speech Recognition (Asr)mentioning

confidence: 98%

Section: Related Work On Automatic Speech Recognition (Asr)mentioning

confidence: 99%

Integrating Eye- and Mouse-Tracking with Assistant Based Speech Recognition for Interaction at Controller Working Positions

2021

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“…The study examined the problems with the path and cycle approach and created a model that provides solutions to the problems. Ohneiser et al (2021) consider ATCos workload as a limiting factor of air traffic capacity. For this reason, the authors tried to predict tower commands with speech recognition applications to reduce the workload and increase the capacity.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The study proved that assistant-based speech recognition significantly reduces ATCos' workload and increases capacity. P erez Moreno et al (2022) proposed a methodology based on machine learning methodology to characterize the complexity of ATC sectors based on individual operations. The methodology can be of significant value to ATC in that when applied to real cases, ATC will be able to anticipate the complexity of the airspace and optimize its resources accordingly.…”

Section: Literature Reviewmentioning

confidence: 99%

An algorithmic application with flexible airspace approach

Kökhan

Gökdalay

2023

AEAT

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Purpose The purpose of this study is to develop an operational level decision support system model for air traffic controllers (ATCos) within the framework of the Flexible Use of Airspace (FUA) concept to enable more efficient use of airspace capacity. This study produces a systematic solution to the route selection process so that the ATCo can determine the most efficient route with an operational decision support system model using Dijkstra’s Shortest Path Algorithm. Design/methodology/approach In this study, a new decision support system model for ATCos in decision-making positions was recommended and used. ATCos use this model as a main model for determining the shortest and safest route for aircraft as an operational-level decision support system. Dijkstra Algorithm, used in the model, is defined step by step and then explained with the pseudocode. Findings It has been determined that when the FUA concept and DSS are used while the ATCo chooses a route, significant fuel, time and capacity savings are achieved in flight operations. Emissions resulting from the negative environmental effects of air transportation are reduced, and significant capacity increase can be achieved. The operational level decision support system developed in the study was tested with 55 scenarios on the Ankara–Izmir flight route compared to the existing fixed route. The results for the proposed most efficient route were achieved at 11.22% distance (nm), 9.36%-time (min) savings and 837.71 kg CO2 emission savings. Originality/value As far as the literature is reviewed, most studies aimed at increasing airspace efficiency produce solutions that try to improve rather than replace the normal process. Considering the literature positioning of this study compared to other studies, the proposed model provides a new systematic solution to the problems that cause human-induced route inefficiency within the framework of the FUA concept.

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“…Linear Discriminant Analysis (LDA) [123] Quadratic Discriminant Analysis (QDA) [123] Gaussian Mixture Model (GMM) [153,190-195] [196-198] Autoencoder [199] Fuzzy Logic [200] [117,201,202] [ [203][204][205][206][207] Logistic Regression [131,150] [126,208] Bayesian Network [30,[209][210][211] Multi-Layer Perceptron (MLP) [125,163,251] Graph-Theoretic Clustering [253] Hidden Markov Model (HMM) [254] Markov Decision Process (MDP) [78,235,240,255] Not referenced [256,257] [258]…”

Section: Categorisation Insightsmentioning

confidence: 99%

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

et al. 2022

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Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed.

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Prediction and extraction of tower controller commands for speech recognition applications

Cited by 14 publications

References 3 publications

Integrating Eye- and Mouse-Tracking with Assistant Based Speech Recognition for Interaction at Controller Working Positions

Integrating Eye- and Mouse-Tracking with Assistant Based Speech Recognition for Interaction at Controller Working Positions

An algorithmic application with flexible airspace approach

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

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