Assessing Safety-Critical Systems from Operational Testing: A Study on Autonomous Vehicles

Xingyu Zhao, Kizito Salako, Lorenzo Strigini, Valentin Robu, David Flynn

Research output: Contribution to journalArticle

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Abstract

Context: Demonstrating high reliability and safety for safety-critical systems (SCSs) remains a hard problem. Diverse evidence needs to be combined in a rigorous way: in particular, results of operational testing with other evidence from design and verification. Growing use of machine learning in SCSs, by precluding most established methods for gaining assurance, makes evidence from operational testing even more important for supporting safety and reliability claims.
Objective: We revisit the problem of using operational testing to demonstrate high reliability. We use Autonomous Vehicles (AVs) as a current example. AVs are making their debut on public roads: methods for assessing whether an AV is safe enough are urgently needed. We demonstrate how to answer 5 questions that would arise in assessing an AV type, starting with those proposed by a highly-cited study.
Method: We apply new theorems extending our Conservative Bayesian Inference (CBI) approach, which exploit the rigour of Bayesian methods while reducing the risk of involuntary misuse associated (we argue) with now-common applications of Bayesian inference; we define additional conditions needed for applying these methods to AVs.
Results: Prior knowledge can bring substantial advantages if the AV design allows strong expectations of safety before road testing. We also show how naive attempts at conservative assessment may lead to over-optimism instead; why extrapolating the trend of disengagements (take-overs by human drivers) is not suitable for safety claims; use of knowledge that an AV has moved to a "less stressful" environment.
Conclusion: While some reliability targets will remain too high to be practically verifiable, our CBI approach removes a major source of doubt: it allows use of prior knowledge without inducing dangerously optimistic biases. For certain ranges of required reliability and prior beliefs, CBI thus supports feasible, sound arguments. Useful conservative claims can be derived from limited prior knowledge.
Original languageEnglish
Article number106393
JournalInformation and Software Technology
Volume128
Early online date19 Aug 2020
DOIs
Publication statusE-pub ahead of print - 19 Aug 2020

Keywords

  • Autonomous systems
  • safety assurance
  • statistical testing
  • safety-critical systems
  • ultra-high reliability
  • Conservative Bayesian Inference
  • AI safety
  • proven in use
  • glgobally at least equivalent
  • software reliability growth models

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