A Mapping of Assurance Techniques for Learning Enabled Autonomous Systems to the Systems Engineering Lifecycle

TL;DR

This paper maps recent assurance and testing techniques for learning-enabled autonomous systems onto the traditional systems engineering lifecycle, aiding professionals in planning and risk communication without overhauling existing processes.

Contribution

It provides a structured mapping of new assurance approaches to the systems engineering V-model, facilitating better integration and understanding for assurance activities.

Findings

Categorizes assurance techniques into development, acquisition, and sustainment phases.

Helps inform comprehensive test and evaluation planning.

Supports objective risk communication to leadership.

Abstract

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.