National Labs Collaborate to Improve Traffic Flow with Vehicle Automation

Researchers use virtual simulations to test merging patterns and optimize energy efficiency.

Published on Feb. 12, 2026

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Researchers from the U.S. Department of Energy's National Transportation Research Center at Oak Ridge National Laboratory are working with other national labs to tackle the problem of rush-hour traffic congestion using cooperative driving automation (CDA) technology. The team is using the CAVE lab and Real-Sim XIL platform to place real vehicles in virtual traffic scenarios, allowing them to model how traffic patterns and infrastructure interact and develop algorithms to smooth merging and keep traffic flowing efficiently.

Why it matters

Improving traffic flow through CDA technology can help reduce fuel consumption, energy waste, and frustration for drivers during their daily commutes. This collaboration between national labs is positioning the U.S. to shape the next generation of intelligent transportation systems.

The details

The research team, which includes experts from ORNL, Argonne National Laboratory, Lawrence Berkeley National Laboratory, and the National Laboratory of the Rockies, is divided into categories based on real-world mobility challenges. ORNL is focusing on cooperative merging, using the CAVE lab and Real-Sim XIL to develop algorithms that smooth the unpredictable stops and starts of human drivers. Other labs are leading car-following studies, modeling and field-test integration, and developing a scalable co-simulation framework to assess communication performance and energy/fuel-saving potential of coordinated vehicle operations.

  • The team earned DOE's Vehicle Technologies Office Team Award for Outstanding Collaboration in 2025.

The players

Adian Cook

ORNL's lead researcher on the project.

ORNL

The U.S. Department of Energy's National Transportation Research Center at Oak Ridge National Laboratory, which is managing the project.

Argonne National Laboratory

One of the national labs collaborating on the project, leading car-following studies.

Lawrence Berkeley National Laboratory

One of the national labs collaborating on the project, advancing modeling and field-test integration.

National Laboratory of the Rockies

One of the national labs collaborating on the project, developing a scalable co-simulation framework.

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What they’re saying

“Autonomous driving often brings to mind self-driving vehicles, but there's also a significant infrastructure piece, such as intelligent signal control or cooperative infrastructure. For example, traffic lights can have optimized signals that may also interact directly with connected vehicles to keep traffic moving along.”

— Adian Cook, ORNL's lead researcher on the project

“When looking at merging, you get these weird bottlenecks because people are braking and making sudden moves that disrupt the flow. With CDA, infrastructure can coordinate with vehicles and traffic patterns to keep everything moving smoothly.”

— Adian Cook, ORNL's lead researcher on the project

“If you're getting through intersections quicker and there's less idle time, you're burning less fuel. Our goal in this project is to optimize energy and overall traffic efficiency.”

— Adian Cook, ORNL's lead researcher on the project

What’s next

The team plans to continue testing and refining their CDA algorithms and infrastructure coordination strategies to further improve traffic flow and energy efficiency.

The takeaway

This collaborative research between national labs is paving the way for the next generation of intelligent transportation systems that can reduce traffic congestion, fuel consumption, and driver frustration on America's roadways.