  Proving ground has been a critical component in testing and validation for
Connected and Automated Vehicles (CAV). Although quite a few world-class
testing facilities have been under construction over the years, the evaluation
of proving grounds themselves as testing approaches has rarely been studied. In
this paper, we present the first attempt to systematically evaluate CAV proving
grounds and contribute to a generative sample-based approach to assessing the
representation of traffic scenarios in proving grounds. Leveraging typical use
cases extracted from naturalistic driving events, we establish a strong link
between proving ground testing results of CAVs and their anticipated public
street performance. We present benchmark results of our approach on three
world-class CAV testing facilities: Mcity, Almono (Uber ATG), and Kcity. We
successfully show the overall evaluation of these proving grounds in terms of
their capability to accommodate real-world traffic scenarios. We believe that
when the effectiveness of a testing ground itself is validated, the testing
results would grant more confidence for CAV public deployment.
