  The acceptance of autonomous vehicles is dependent on the rigorous assessment
of their safety. Furthermore, the commercial viability of AV programs depends
on the ability to estimate the time and resources required to achieve desired
safety levels. Naive approaches to estimating the reliability and safety levels
of autonomous vehicles under development are will require infeasible amounts of
testing of a static vehicle configuration. To permit both the estimation of
current safety, and make predictions about the reliability of future systems, I
propose the use of a standard tool for modelling the reliability of evolving
software systems, software reliability growth models (SRGMs).
  Publicly available data from Californian public-road testing of two
autonomous vehicle systems is modelled using two of the best-known SRGMs. The
ability of the models to accurately estimate current reliability, as well as
for current testing data to predict reliability in the future after additional
testing, is evaluated. One of the models, the Musa-Okumoto model, appears to be
a good estimator and a reasonable predictor.
