QUASAR: Quantifiable Assurance Cases for Trusted Autonomy

Abstract

The goal of QUASAR was to develop techniques and tool support for the construction of dynamic assurance cases (DACs) that address both static design-time concerns and dynamic operational concerns and, for the latter, to provide quantifiable and executable measures of confidence in appropriate assurance properties of the target platforms. In particular, the project focused on applying these techniques to learning-enabled components (LECs) that comprise key parts of autonomous systems, and to that end, we used these techniques to construct platform-specific assurance cases in collaboration with our TA4 partners, Boeing (air domain) and Northrop Grumman (undersea domain).

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2023
Accession Number
AD1209631

Entities

People

  • Ewen Denney
  • Ganesh J. Pai
  • Irfan Sljivo
  • Rebecca Lee

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Cyber
  • Energy and Power Technologies
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Artificial Intelligence
  • Autonomous Underwater Vehicles
  • Bayesian Networks
  • Collision Avoidance
  • Computational Science
  • Control Systems
  • Detection
  • Detectors
  • Information Science
  • Information Systems
  • Machine Learning
  • Neural Networks
  • Ontologies
  • Risk Analysis
  • Three Dimensional

Fields of Study

  • Computer science

Readers

  • Astronomy/Astrophysics
  • Cybersecurity.
  • Software Engineering

Technology Areas

  • Autonomy
  • Autonomy - Autonomous System Control