Analysis techniques of autonomy framework metrics for autonomous developers
George Hwang, Aniruddha Katre, Kyle Hart, Charles Rea · 2022
As autonomous systems proliferate, empirical measurement of their fitness is paramount. Several frameworks have been developed that provide guidance on what should be measured. However, these frameworks require users to develop their own metrics. Additionally, these frameworks focus on the autonomous systems rather than the enablers. An enabler could be the process used by developers. This research introduces novel techniques to analyze metrics used to measure fitness of autonomy architectures for developers. Crucially, this will be generalizable across autonomy measurement frameworks. The results are new techniques acquisition professionals can use to help better make tradeoffs development-wise for different architectures.