  As autonomous vehicles (AVs) take on growing Operational Design Domains
(ODDs), they need to go through a systematic, transparent, and scalable
evaluation process to demonstrate their benefits to society. Current scenario
sampling techniques for AV performance evaluation usually focus on a specific
functionality, such as lane changing, and do not accommodate a transfer of
information about an AV system from one ODD to the next. In this paper, we
reformulate the scenario sampling problem across ODDs and functionalities as a
submodular optimization problem. To do so, we abstract AV performance as a
Bayesian Hierarchical Model, which we use to infer information gained by
revealing performance in new scenarios. We propose the information gain as a
measure of scenario relevance and evaluation progress. Furthermore, we leverage
the submodularity, or diminishing returns, property of the information gain not
only to find a near-optimal scenario set, but also to propose a stopping
criterion for an AV performance evaluation campaign. We find that we only need
to explore about 7.5% of the scenario space to meet this criterion, a 23%
improvement over Latin Hypercube Sampling.
