  Existing data collection methods for traffic operations and control usually
rely on infrastructure-based loop detectors or probe vehicle trajectories.
Connected and automated vehicles (CAVs) not only can report data about
themselves but also can provide the status of all detected surrounding
vehicles. Integration of perception data from multiple CAVs as well as
infrastructure sensors (e.g., LiDAR) can provide richer information even under
a very low penetration rate. This paper aims to develop a cooperative data
collection system, which integrates Lidar point cloud data from both
infrastructure and CAVs to create a cooperative perception environment for
various transportation applications. The state-of-the-art 3D detection models
are applied to detect vehicles in the merged point cloud. We test the proposed
cooperative perception environment with the max pressure adaptive signal
control model in a co-simulation platform with CARLA and SUMO. Results show
that very low penetration rates of CAV plus an infrastructure sensor are
sufficient to achieve comparable performance with 30% or higher penetration
rates of connected vehicles (CV). We also show the equivalent CV penetration
rate (E-CVPR) under different CAV penetration rates to demonstrate the data
collection efficiency of the cooperative perception environment.
