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18 May 2020 Test and evaluation needs of multi-domain autonomous systems
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Abstract
The paper highlights the need for methods of analytical science for multi-domain autonomy evaluation. Multidomain autonomous systems need to collect large amounts of data to verify, validate, test, and evaluate system operations. For multi-domain and uncertain scenarios, data sampling may not be adequate to fully explore and represent the entire trade space for verification and validation (V&V). However, leveraging methods from test and evaluation (T&E), a hierarchy of analytics can be developed so as to narrow the trade space. Issues in V&V/T&E employ statistics, but could benefit from first-principles physics theoretical analytics, data augmentation, and scenario design. The use of modeling is not new; however, as analytics of artificial intelligence and machine learning (AI/ML) are designed to exploit data; then there are opportunities to allow one domain (e.g., air) support data analytics in another domain (e.g., cyber).
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Erik Blasch, Yu Chen, Dan Shen, and Genshe Chen "Test and evaluation needs of multi-domain autonomous systems", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 1141308 (18 May 2020); https://doi.org/10.1117/12.2559031
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