  Proving ground, or on-track testing has been an essential part of testing and
validation process for connected and autonomous vehicles (CAV). Several
world-class CAV proving grounds, such as Mcity at the University of Michigan
and The Castle of Waymo, have already been built, and many more are currently
under construction. In this paper, we propose the first optimization approach
to CAV proving ground designing and refer to any such CAV-centric design
problem as "Xcity" to emphasize the enormous investment, the multi-dimensional
spatial consideration, and the immense construction effort emerging globally.
Inspired by the recent progress on traffic encounter clustering, we further
define "road assets" as fundamental building blocks and formulate the whole
design process into nonlinear optimization problems. We have shown that such
framework can be utilized to adaptively generate CAV proving ground designs
with optimized capability and flexibility and can further be extended to
evaluate an existing "Xcity" design.
