Abstract
Final submission to 2022 IEEE International Conference on Assured
Autonomy Learning enabled autonomous systems provide increased capabilities compared
to traditional systems. However, the complexity of and probabilistic nature in
the underlying methods enabling such capabilities present challenges for
current systems engineering processes for assurance, and test, evaluation,
verification, and validation (TEVV). This paper provides a preliminary attempt
to map recently developed technical approaches in the assurance and TEVV of
learning enabled autonomous systems (LEAS) literature to a traditional systems
engineering v-model. This mapping categorizes such techniques into three main
approaches: development, acquisition, and sustainment. We review the latest
techniques to develop safe, reliable, and resilient learning enabled autonomous
systems, without recommending radical and impractical changes to existing
systems engineering processes. By performing this mapping, we seek to assist
acquisition professionals by (i) informing comprehensive test and evaluation
planning, and (ii) objectively communicating risk to leaders.