  Various automobile and mobility companies, for instance Ford, Uber and Waymo,
are currently testing their pre-produced autonomous vehicle (AV) fleets on the
public roads. However, due to rareness of the safety-critical cases and,
effectively, unlimited number of possible traffic scenarios, these on-road
testing efforts have been acknowledged as tedious, costly, and risky. In this
study, we propose Accelerated De- ployment framework to safely and efficiently
estimate the AVs performance on public streets. We showed that by appropriately
addressing the gradual accuracy improvement and adaptively selecting meaningful
and safe environment under which the AV is deployed, the proposed framework
yield to highly accurate estimation with much faster evaluation time, and more
importantly, lower deployment risk. Our findings provide an answer to the
currently heated and active discussions on how to properly test AV performance
on public roads so as to achieve safe, efficient, and statistically-reliable
testing framework for AV technologies.
