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Guidelines and Regulatory Framework for Machine Learning in Aviation

Torens, Christoph and Durak, Umut and Dauer, Johann C. (2022) Guidelines and Regulatory Framework for Machine Learning in Aviation. In: AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022. AIAA SCITECH 2022 Forum, 2022-01-03 - 2022-01-07, San Diego, California. doi: 10.2514/6.2022-1132. ISBN 978-162410631-6.

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Official URL: https://arc.aiaa.org/doi/10.2514/6.2022-1132


Automation and eventually autonomy are regarded as the enabler for upcoming Urban Air Mobility (UAM) / Advanced Air Mobility segment. Only they could enable unprecedented opportunities for scaling drones and air taxis to a large number of vehicles, making the services available for everyone. Artificial Intelligence (AI) in general, Machine Learning (ML) in particular promise a huge leap towards achieving high levels of automation and further autonomy. Nevertheless, the safety concerns and challenges regarding compliance to the existing software standards are more pressing then ever before. Existing regulatory framework for hardware and software items fail to provide adequate acceptable means of compliance for AI-based systems. Hence, there are currently a number of ongoing efforts to update and augment the current standards. This paper will give an overview of the existing and upcoming regulatory framework for certifying AI-based systems. It will elaborate the EASA documents, artificial intelligence roadmap, Concepts of Design Assurance for Neural Networks (CoDANN), CoDANN II, as well as the concept paper on first usable guidance for level I machine learning applications. Furthermore, suitable guidance from EuroCAE, RTCA, ASTM and AVSI will be discussed.

Item URL in elib:https://elib.dlr.de/148340/
Document Type:Conference or Workshop Item (Speech)
Title:Guidelines and Regulatory Framework for Machine Learning in Aviation
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Torens, ChristophUNSPECIFIEDhttps://orcid.org/0000-0002-0651-4390UNSPECIFIED
Durak, UmutUNSPECIFIEDhttps://orcid.org/0000-0002-2928-1710UNSPECIFIED
Dauer, Johann C.UNSPECIFIEDhttps://orcid.org/0000-0002-8287-2376UNSPECIFIED
Date:3 January 2022
Journal or Publication Title:AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:guidelines, standards, machine learning, certification, artificial intelligence, safety-critical, aviation
Event Title:AIAA SCITECH 2022 Forum
Event Location:San Diego, California
Event Type:international Conference
Event Start Date:3 January 2022
Event End Date:7 January 2022
Organizer:AIAA SCITECH Forum
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Components and Systems
DLR - Research area:Aeronautics
DLR - Program:L CS - Components and Systems
DLR - Research theme (Project):L - Unmanned Aerial Systems
Location: Braunschweig
Institutes and Institutions:Institute of Flight Systems > Unmanned Aircraft
Institute of Flight Systems
Deposited By: Torens, Christoph
Deposited On:01 Feb 2022 13:49
Last Modified:24 Apr 2024 20:46

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