Chapter
Aug 31, 2022

Concept of Operations of Next-Generation Traffic Control Utilizing Infrastructure-Based Cooperative Perception

Publication: International Conference on Transportation and Development 2022

ABSTRACT

This paper provides a system architecture for an infrastructure-based cooperative perception fusion engine for next-generation traffic control. This engine will provide a complete state-space digital representation with measurable accuracy to support a wide-range of applications. The architecture includes inputs, functional flow, data standardization recommendations, outputs, and supported applications. The cooperative perception engine addresses critical needs with respect to accelerating the benefits of automation through intelligent roadway infrastructure, which complements and accelerates connected and automated vehicle (CAV) technology. The cooperative perception acquires and fuses information from sensors (radar, LiDAR, and cameras) and CAVs to perceive roadway traffic states of moving objects, creates a complete 3D digital representation of that state-space, and communicates it to downstream application such as intelligent signal control, safety and energy applications, and cooperate driving applications. The intelligent roadway infrastructure approach, as opposed to a vehicle-centric approach, is more scalable because it can be deployed to the roughly 300,000 signalized intersections more readily than over 300 million vehicles in the United States, and accrues early-stage benefits equitable to all roadway users addressing safety, equity, fuel efficiency, and greenhouse gas reduction.

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International Conference on Transportation and Development 2022
Pages: 93 - 104

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Published online: Aug 31, 2022

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Stanley E. Young, Ph.D. [email protected]
P.E.
1Center for Integrated Mobility Sciences, National Renewable Energy Laboratory, Golden, CO. Email: [email protected]
Erik A. Bensen [email protected]
2Computational Science Center, National Renewable Energy Laboratory, Golden, CO. Email: [email protected]
Lei Zhu, Ph.D. [email protected]
3Dept. of Systems Engineering and Engineering Management, Univ. of North Carolina at Charlotte, Charlotte, NC. Email: [email protected]
Christopher Day, Ph.D. [email protected]
4Dept. of Civil, Construction, and Environmental Engineering, Iowa State Univ., Ames, IA. Email: [email protected]
J. Sam Lott [email protected]
5Automated Mobility Systems, LLC, Houston, TX. Email: [email protected]
Rimple Sandhu, Ph.D. [email protected]
6National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO. Email: [email protected]
Charles Tripp, Ph.D. [email protected]
7Computational Science Center, National Renewable Energy Laboratory, Golden, CO. Email: [email protected]
Peter Graf, Ph.D. [email protected]
8Computational Science Center, National Renewable Energy Laboratory, Golden, CO. Email: [email protected]

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