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