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Machine Learning Verification and Safety for Unmanned Aircraft - A Literature Study

Torens, Christoph and Jünger, Franz and Schirmer, Sebastian and Schopferer, Simon and Maienschein, Theresa Diana and Dauer, Johann C. (2022) Machine Learning Verification and Safety for Unmanned Aircraft - A Literature Study. In: AIAA SciTech 2022 Forum. AIAA SCITECH 2022 Forum, 2022-01-03 - 2022-01-07, San Diego, California. doi: 10.2514/6.2022-1133. ISBN 978-162410631-6.

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Official URL: https://arc.aiaa.org/doi/10.2514/6.2022-1133


Machine learning (ML) has proven to be the tool of choice for achieving human-like or even super-human performance with automation on specific tasks. As a result, this data-driven approach is currently experiencing massive interest in all industry domains. This increased use also applies for the safety critical aviation domain. With no human pilot on board, the potential use cases of ML for unmanned aircraft are particularly promising. Even upcoming Urban Air Mobility (UAM) concepts are planning to remove the onboard pilot and instead use ML to support a remote pilot, possibly supervising a fleet of vehicles. However, the verification of ML algorithms is a challenging problem, since established safety standards and assurance methods are not applicable. Thus, this work comprises a literature study on the topic of ML verification and safety. This research paper uses a systematic approach to map and categorize the research and focus on specific subtopics that are of particular interest in the context of existing guidance documents.

Item URL in elib:https://elib.dlr.de/148341/
Document Type:Conference or Workshop Item (Speech)
Title:Machine Learning Verification and Safety for Unmanned Aircraft - A Literature Study
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Torens, ChristophUNSPECIFIEDhttps://orcid.org/0000-0002-0651-4390UNSPECIFIED
Schirmer, SebastianUNSPECIFIEDhttps://orcid.org/0000-0002-4596-2479UNSPECIFIED
Schopferer, SimonUNSPECIFIEDhttps://orcid.org/0000-0001-5254-3961UNSPECIFIED
Dauer, Johann C.UNSPECIFIEDhttps://orcid.org/0000-0002-8287-2376UNSPECIFIED
Date:3 January 2022
Journal or Publication Title:AIAA SciTech 2022 Forum
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:literature study, machine learning, verification and validation, certification, safety-critical, taxonomy, artificial intelligence
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 2022 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 13:38
Last Modified:24 Apr 2024 20:46

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