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Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing

Zhao, X., Robu, V., Flynn, D. , Salako, K. ORCID: 0000-0003-0394-7833 & Strigini, L. (2020). Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing. 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE), pp. 13-23. ISSN 2332-6549 doi: 10.1109/ISSRE.2019.00012


There is an urgent societal need to assess whether
autonomous vehicles (AVs) are safe enough. From published
quantitative safety and reliability assessments of AVs, we know
that, given the goal of predicting very low rates of accidents,
road testing alone requires infeasible numbers of miles to
be driven. However, previous analyses do not consider any
knowledge prior to road testing – knowledge which could bring
substantial advantages if the AV design allows strong expectations
of safety before road testing. We present the advantages of a new
variant of Conservative Bayesian Inference (CBI), which uses
prior knowledge while avoiding optimistic biases. We then study
the trend of disengagements (take-overs by human drivers) by
applying Software Reliability Growth Models (SRGMs) to data
from Waymo’s public road testing over 51 months, in view of the
practice of software updates during this testing. Our approach is
to not trust any specific SRGM, but to assess forecast accuracy
and then improve forecasts. We show that, coupled with accuracy
assessment and recalibration techniques, SRGMs could be a
valuable test planning aid.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: autonomous vehicles, reliability claims, statistical testing, safety-critical systems, ultra-high reliability, conservative Bayesian inference, software reliability growth models
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Departments: School of Science & Technology > Computer Science
School of Science & Technology > Computer Science > Software Reliability
Text (Conference Best Paper Nominee) - Accepted Version
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