  Endowed with automation and connectivity, Connected and Automated Vehicles
are meant to be a revolutionary promoter for Cooperative Driving Automation.
Nevertheless, CAVs need high-fidelity perception information on their
surroundings, which is available but costly to collect from various onboard
sensors as well as vehicle-to-everything (V2X) communications. Therefore,
authentic perception information based on high-fidelity sensors via a
cost-effective platform is crucial for enabling CDA-related research, e.g.,
cooperative decision-making or control. Most state-of-the-art traffic
simulation studies for CAVs rely on situation-awareness information by directly
calling on intrinsic attributes of the objects, which impedes the reliability
and fidelity of the assessment of CDA algorithms. In this study, a
\textit{Cyber Mobility Mirror (CMM)} Co-Simulation Platform is designed for
enabling CDA by providing authentic perception information. The \textit{CMM}
Co-Simulation Platform can emulate the real world with a high-fidelity sensor
perception system and a cyber world with a real-time rebuilding system acting
as a "\textit{Mirror}" of the real-world environment. Concretely, the
real-world simulator is mainly in charge of simulating the traffic environment,
sensors, as well as the authentic perception process. The mirror-world
simulator is responsible for rebuilding objects and providing their information
as intrinsic attributes of the simulator to support the development and
evaluation of CDA algorithms. To illustrate the functionality of the proposed
co-simulation platform, a roadside LiDAR-based vehicle perception system for
enabling CDA is prototyped as a study case. Specific traffic environments and
CDA tasks are designed for experiments whose results are demonstrated and
analyzed to show the performance of the platform.
