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

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

Christian Ellis, Maggie Wigness and Lance Fiondella
2022 IEEE INTERNATIONAL CONFERENCE ON ASSURED AUTONOMY (ICAA 2022), pp.28-35
01/01/2022

Abstract

Automation & Control Systems Computer Science, Artificial Intelligence Engineering, Electrical & Electronic Science & Technology Computer Science Engineering Technology
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. This mapping reviews 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.
url
https://arxiv.org/pdf/2301.00057View
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