  The promise of increased road safety is a key motivator for the development
of automated vehicles (AV). Yet, demonstrating that an AV is as safe as, or
even safer than, a human-driven vehicle has proven to be challenging. Should an
AV be examined purely virtually, allowing large numbers of fully controllable
tests? Or should it be tested under real environmental conditions on a proving
ground? Since different test setups have different strengths and weaknesses, it
is still an open question how virtual and real tests should be combined. On the
way to answer this question, this paper proposes transfer importance sampling
(TIS), a risk estimation method linking different test setups. Fusing the
concepts of transfer learning and importance sampling, TIS uses a scalable,
cost-effective test setup to comprehensively explore an AV's behavior. The
insights gained then allow parameterizing tests in a more trustworthy test
setup accurately reflecting risks. We show that when using a trustworthy test
setup alone is prohibitively expensive, linking it to a scalable test setup can
increase efficiency $\unicode{x2013}$ without sacrificing the result's
validity. Thus, the test setups' individual deficiencies are compensated for by
their systematic linkage.
