Machine Learning Verification and Safety for Unmanned Aircraft - A Literature Study
Christoph Torens, Franz Juenger, Sebastian Schirmer, Simon Schopferer, Theresa Maienschein, Johann C. Dauer
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-1133.vid 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.