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Ensuring Safety for Artificial-Intelligence-Based Automatic Speech Recognition in Air Traffic Control Environment

Pinska-Chauvin, Ella and Helmke, Hartmut and Dokic, Jelena and Hartikainen, Petri and Ohneiser, Oliver and Garcia Lasheras, Raquel (2023) Ensuring Safety for Artificial-Intelligence-Based Automatic Speech Recognition in Air Traffic Control Environment. Aerospace, 10 (11), pp. 1-23. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/aerospace10110941. ISSN 2226-4310.

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Official URL: https://www.mdpi.com/2226-4310/10/11/941

Abstract

This paper describes the safety assessment conducted in SESAR2020 project PJ.10-W2-96 ASR on automatic speech recognition (ASR) technology implemented for air traffic control (ATC) centers. ASR already now enables the automatic recognition of aircraft callsigns and various ATC commands including command types based on controller–pilot voice communications for presentation at the controller working position. The presented safety assessment process consists of defining design requirements for ASR technology application in normal, abnormal, and degraded modes of ATC operations. A total of eight functional hazards were identified based on the analysis of four use cases. The safety assessment was supported by top-down and bottom-up modelling and analysis of the causes of hazards to derive system design requirements for the purposes of mitigating the hazards. Assessment of achieving the specified design requirements was supported by evidence generated from two real-time simulations with pre-industrial ASR prototypes in approach and en-route operational environments. The simulations, focusing especially on the safety aspects of ASR application, also validated the hypotheses that ASR reduces controllers’ workload and increases situational awareness. The missing validation element, i.e., an analysis of the safety effects of ASR in ATC, is the focus of this paper. As a result of the safety assessment activities, mitigations were derived for each hazard, demonstrating that the use of ASR does not increase safety risks and is, therefore, ready for industrialization.

Item URL in elib:https://elib.dlr.de/198800/
Document Type:Article
Title:Ensuring Safety for Artificial-Intelligence-Based Automatic Speech Recognition in Air Traffic Control Environment
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Pinska-Chauvin, EllaIntegra Consult A/SUNSPECIFIEDUNSPECIFIED
Helmke, HartmutUNSPECIFIEDhttps://orcid.org/0000-0002-1939-0200UNSPECIFIED
Dokic, JelenaIntegra Consult A/SUNSPECIFIEDUNSPECIFIED
Hartikainen, PetriIntegra Consult A/SUNSPECIFIEDUNSPECIFIED
Ohneiser, OliverUNSPECIFIEDhttps://orcid.org/0000-0002-5411-691XUNSPECIFIED
Garcia Lasheras, RaquelCRIDAUNSPECIFIEDUNSPECIFIED
Date:3 November 2023
Journal or Publication Title:Aerospace
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:10
DOI:10.3390/aerospace10110941
Page Range:pp. 1-23
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Series Name:Special Issue Automatic Speech Recognition and Understanding in Air Traffic Management
ISSN:2226-4310
Status:Published
Keywords:safety assessment; air traffic control; automatic speech recognition; workload; situational awareness; en-route sector; approach sector
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Air Transportation and Impact
DLR - Research area:Aeronautics
DLR - Program:L AI - Air Transportation and Impact
DLR - Research theme (Project):L - Integrated Flight Guidance
Location: Braunschweig
Institutes and Institutions:Institute of Flight Guidance > Controller Assistance
Deposited By: Ohneiser, Oliver
Deposited On:06 Nov 2023 07:55
Last Modified:06 Nov 2023 07:55

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