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

Zhao, X., Salako, K. ORCID: 0000-0003-0394-7833, Strigini, L. ORCID: 0000-0002-4246-2866, Robu, V. and Flynn, D. (2020). Assessing Safety-Critical Systems from Operational Testing: A Study on Autonomous Vehicles. Information and Software Technology, 106393.. doi: 10.1016/j.infsof.2020.106393

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.

Publication Type: Article
Additional Information: © 2020 The Authors. Published by Elsevier B. V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Publisher Keywords: Autonomous systems, safety assurance, statistical testing, safety-critical systems, ultra-high reliability, conservativeBayesian inference, AI safety, proven in use, globally at least equivalent, software reliability growth models.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
School of Mathematics, Computer Science & Engineering > Computer Science > Software Reliability
Date Deposited: 19 Aug 2020 10:06
URI: https://openaccess.city.ac.uk/id/eprint/24779
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