  Object perception plays a fundamental role in Cooperative Driving Automation
(CDA) which is regarded as a revolutionary promoter for the next-generation
transportation systems. However, the vehicle-based perception may suffer from
the limited sensing range and occlusion as well as low penetration rates in
connectivity. In this paper, we propose Cyber Mobility Mirror (CMM), a
next-generation real-time traffic surveillance system for 3D object perception
and reconstruction, to explore the potential of roadside sensors for enabling
CDA in the real world. The CMM system consists of six main components: 1) the
data pre-processor to retrieve and preprocess the raw data; 2) the roadside 3D
object detector to generate 3D detection results; 3) the multi-object tracker
to identify detected objects; 4) the global locator to map positioning
information from the LiDAR coordinate to geographic coordinate using coordinate
transformation; 5) the cloud-based communicator to transmit perception
information from roadside sensors to equipped vehicles, and 6) the onboard
advisor to reconstruct and display the real-time traffic conditions via
Graphical User Interface (GUI). In this study, a field-operational system is
deployed at a real-world intersection, University Avenue and Iowa Avenue in
Riverside, California to assess the feasibility and performance of our CMM
system. Results from field tests demonstrate that our CMM prototype system can
provide satisfactory perception performance with 96.99% precision and 83.62%
recall. High-fidelity real-time traffic conditions (at the object level) can be
geo-localized with an average error of 0.14m and displayed on the GUI of the
equipped vehicle with a frequency of 3-4 Hz.
