A Cooperative Perception Environment for Traffic Operations and Control

ABSTRACT Existing data collection methods for traffic operations and control usually rely on infrastructurebased 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. INTRODUCTION Traffic operations and control applications (e.g., actuated/adaptive traffic signal control) require real-time traffic information. Traditional infrastructure-based sensor systems such as loopdetectors and traffic cameras have been widely implemented in the field for decades. Infrastructure-based sense systems usually have relatively high installation and maintenance costs. More importantly, data collected from traditional infrastructure-based sensors is location-specific, which does not reflect the whole spatial distribution of vehicles.