Abstract
This paper puts forth a system architecture for an infrastructure-based cooperative perception (CP) fusion engine, to provide a complete state-space digital representation, with measurable accuracy, to support a wide-range of applications. The architecture includes the inputs, functional flow, data standardization recommendations, outputs and supported applications. The CP engine addresses critical needs with respect to accelerating the benefits of automation through intelligent roadway infrastructure (IRI), that complements and accelerates connected and automated vehicle (CAV) technology. that the CP acquires and fuses information from sensors (radar, LiDAR, and cameras), and CAVs to intelligently perceive roadway traffic states of all moving objects, create a complete three-dimensional digital representation of that state-space, and communicate it to downstream application such as intelligent signal control, safety and energy applications, and cooperate driving applications for CAVs as examples. The IRI approach, as opposed to a vehicle centric approach, is found to be more scalable in that it can deployed to the roughly 300,000 signalized intersections more readily than the over 300 million vehicles in the US, and accrues early-stage benefits equitable to all roadway users addressing safety, equity, fuel efficiency, and GHG reduction.
Original language | American English |
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Number of pages | 15 |
State | Published - 2022 |
Event | ASCE/ICTD 2022 - Seattle, WA Duration: 31 May 2022 → 3 Jun 2022 |
Conference
Conference | ASCE/ICTD 2022 |
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City | Seattle, WA |
Period | 31/05/22 → 3/06/22 |
Bibliographical note
See NREL/CP-5400-84534 for paper as published in proceedingsNREL Publication Number
- NREL/CP-5400-81978
Keywords
- data fusion
- infrastructure to infrastructure
- intelligent roadway infrastructure
- track fusion
- traffic control
- vehicle to infrastructure