A Cooperative Perception Environment for Traffic Operations and Control
Hanlin Chen, Brian Liu, Xumiao Zhang, Feng Qian, Z. Morley Mao, and, Yiheng Feng

TL;DR
This paper develops a cooperative perception system combining infrastructure and CAV sensor data, enhancing traffic data collection efficiency even at low CAV penetration rates, and demonstrates its effectiveness through simulation.
Contribution
It introduces a novel cooperative perception environment integrating Lidar data from infrastructure and CAVs for improved traffic management.
Findings
Low CAV and infrastructure sensor penetration achieve performance comparable to higher CV penetration.
The cooperative perception system improves data collection efficiency.
Simulation results validate the system's effectiveness in traffic signal control.
Abstract
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…
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Taxonomy
TopicsTraffic Prediction and Management Techniques · Advanced Optical Sensing Technologies · Autonomous Vehicle Technology and Safety
