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U.S. Department of Transportation U.S. Department of Transportation Icon United States Department of Transportation United States Department of Transportation

Public Roads - Spring 2022

Spring 2022
Issue No:
Vol. 86 No. 1
Publication Number:
Table of Contents

Cooperative Driving Automation: Reducing Traffic Congestion

by Pavle Bujanović and Steven Vu

Every day, across our Nation’s roadways, drivers experience traffic congestion to and from their destinations. Such congestion impedes safe and efficient travel, often resulting in longer commutes, more crashes, heightened levels of air pollution, and increased risks to mental health. Traffic congestion is an ongoing transportation challenge for transportation researchers and leaders as roadways are becoming more populated and will eventually be unable to efficiently support the increased demand.

Congestion occurs when the demand for using the roadway exceeds the capacity, resulting in slower speeds—sometimes even complete stops—in the flow of traffic. There are two types of congestion:

  • Recurring congestion occurs when the density of vehicles on the road exceeds a certain threshold. As implied by the term “recurring,” this type of congestion does not have an obvious cause, but rather, occurs periodically during peak hours of travel.
  • Nonrecurring congestion occurs due to a certain event (e.g., vehicle crash, inclement weather, road work).

Cooperative driving automation (CDA) offers a potential solution for reducing congestion. As defined by the SAE International (SAE) J3216 standard, CDA enables communication and cooperation between vehicles equipped with driving automation features, other road users, and transportation infrastructure. CDA is poised to transform the current transportation system.

"A photo of a CARMA Platform-equipped vehicle entering an intersection during a green signal. Image Source: FHWA."
Intelligent infrastructure communicates with a CARMA Platform℠- equipped vehicle at an intersection.

The U.S. Department of Transportation Federal Highway Administration’s CDA Program is currently pursuing several research tracks to test CDA features that support transportation systems management and operations (TSMO). These research tracks leverage partnerships with Federal agencies and stakeholders to demonstrate the potential of CDA technologies to improve the transportation system.

FHWA’s CDA research is enabled by the CARMA Ecosystem, a network of open-source software (OSS) products and evaluation tools. The CARMA Ecosystem is helping uncover opportunities to use CDA to improve transportation system safety and performance. The CDA Program leverages OSS research products, including CARMA Platform℠, which is installed in research vehicles to equip them with cooperative capabilities for SAE automated driving system Level 3+ functionality.

“CDA technologies have the potential to improve safety and reduce congestion by enabling vehicles to work together more efficiently. We’re excited to see the CARMA tools that the FHWA’s CDA Program has developed to support CDA research, as this open-source effort benefits everyone,” says Amanda Hamm, program manager of the Virginia Department of Transportation’s Connected and Automated Vehicle Program.

CDA Reliability Research Track

The CDA Reliability research track, in partnership with the Intelligent Transportation Systems Joint Program Office (ITS JPO) and the Federal Motor Carrier Safety Administration (FMCSA), focuses on developing and testing CDA applications that will increase the reliability of the transportation network during nonrecurring traffic congestion scenarios, scenarios that are known, historically, to be unreliable. Under the Reliability research track, the research team recently finished testing the following use cases:

  • Road weather management (RWM): This use case demonstrates interaction between infrastructure and CARMA Platform-equipped vehicles to manage traffic caused by inclement weather, such as a storm or ice on the road.
  • Traffic incident management (TIM): This use case demonstrates the “Move Over” law, in which a traveling vehicle must move over to the next lane when approaching an emergency vehicle pulled over on the shoulder.
  • Work zone management (WZM): This use case demonstrates CDA in a two-lane, two-way road, where a work zone has closed one of the lanes.

Testing for these use cases aims to emulate these real-world scenarios in a closed and controlled test track environment.

CDA Traffic Research Track

In partnership with ITS JPO, the Federal Transit Administration (FTA), and FMCSA, FHWA’s CDA Traffic research track explores the application of CDA to recurring traffic congestion on freeways and arterials through several use cases, the first being the basic travel use case.

Basic Travel Use Case

This use case demonstrates how CDA can enhance traditional TSMO strategies and existing infrastructure for basic travel on freeways and arterials. The CDA features developed from this use case may improve efficiency and safety and reduce bottlenecks at merge points along a freeway. This use case is the first test of CDA technologies in which the vehicles are in complete control of lateral and longitudinal movement (i.e., no human driver controlling steering) while interacting with the surrounding infrastructure. Successful initial testing of the basic travel use case will facilitate further expansion of this use case as well as proof-of-concept testing of other CDA applications.

The concept-of-operations testing under the basic travel use case involves two CARMA Platform-equipped vehicles traveling in the same direction on a freeway. In this use case, one of the vehicles (Vehicle A) will be traveling on the freeway, while the other vehicle (Vehicle B) will be merging onto the freeway from the onramp, requiring vehicle-to-vehicle communication.

This use case demonstrates three CDA features: cooperative ramp merge, platooning, and speed advisory. To accomplish these actions, CARMA Platform-equipped vehicles must communicate with CARMA Cloud℠ (the cloud-based OSS enabling communication and cooperation between the transportation system and users) to make sure the vehicle can merge safely. Additionally, CARMA Cloud broadcasts a speed advisory that the vehicles receive through a nearby roadside unit.

"1. A drawing labeled “Vehicles on their respective routes” shows car B attempting to merge from an onramp into the right lane, which is occupied by car A. Both cars are labeled with the symbol that indicates wireless communication. Arrows show that the two cars are traveling in the same direction. Image Source: FHWA."
1. Vehicles en route on a freeway.
"2. In this drawing, a car labeled A is traveling in the right lane of a highway just past a highway onramp. A note attached to car A reads “Vehicle on its respective route.” A second car, labeled B, is shown on an onramp. An arrow indicates it will enter the highway and into the lane behind car A. A note attached to car B reads “Obstructed lane merge.” Both cars are labeled with the symbol that indicates wireless communication. Image Source: FHWA."
2. Vehicles performing cooperative lane merge.
"3. In this drawing two cars, labeled A and B, are traveling in the same lane on a highway. Both cars are also labeled with the symbol that indicates wireless communication. A note attached to car A reads “Lead vehicle starts the platoon,” while a note attached to car B reads “Rear vehicle joins the platoon.” Image Source: FHWA."
3. Vehicles forming a platoon.
"4. In this drawing, two cars, labeled A and B, are traveling in the same lane in the same direction. Both cars are also labeled with the symbol that indicates wireless communication. Car A is in front of car B. A thought bubble near car A reads “Speed advisory” that is broadcast from CARMA Cloud and a note attached to car A reads “Lead vehicle receives a speed advisory.” Image Source: FHWA."
4. Platoon responding to a speed advisory.

Validation Testing

In September 2021, FHWA’s CDA research team traveled to the American Center for Mobility in Ypsilanti, MI, to perform validation testing for the basic travel use case. The testing took place on a closed test track and involved two CARMA Platform-equipped vehicles, with the use case proceeding as follows:

Vehicle A, the lead vehicle, began its route on the test track at a speed of 25 miles per hour (mph). Vehicle B, the follow vehicle, entered from the ramp and performed an unobstructed lane merge at a speed of 25 mph. This differed slightly from the concept-of-operations testing use case, in which an obstructed lane merge was performed. During the validation testing, Vehicle B’s release from the ramp was manually timed, and thus the merging process did not require any coordination between the vehicles.

"This photo shows a CARMA Platform-equipped vehicle entering a roadway from an onramp. A label on the photo reads “Follow vehicle merges from ramp.” Image Source: FHWA."
CARMA Platform-equipped vehicle merges onto a freeway during validation testing.

After the merge was complete, the two vehicles formed a platoon and proceeded to drive as a platoon along the track. After the vehicles had been platooning for about 0.4 miles (0.6 km), Vehicle A received a speed advisory notice to be applied within a particular area. The speed advisory was broadcasted from a roadside unit connected to CARMA Cloud and instructed Vehicle A to slow its speed to 15 mph. Vehicle A subsequently communicated this speed change to Vehicle B. The vehicles decelerated together to 15 mph and continued to drive as a platoon until they reached the end of the route, at which point the platoon dissolved.

"1. This photo shows two CARMA Platform-equipped vehicles traveling on a roadway in the same lane in the same direction. A label on the photo reads “Vehicles begin platooning after the merge.” Image Source: FHWA."
1. Vehicles form a platoon after the merge.
"2. This photo shows two CARMA Platform-equipped vehicles traveling on a roadway in the same lane in the same direction. A shaded area in the lane is labeled “Speed reduction broadcasted by CARMA Cloud.” Image Source: FHWA."
2. Lead vehicle receives speed advisory broadcasted by CARMA Cloud.


The CARMA OSS approach fosters and encourages collaboration between relevant stakeholders in Government, academia, consulting, and the technical industry to accelerate advancements in CDA research, development, and testing. The source code for CARMA products can be accessed on GitHub at

The CDA engagement efforts provide additional opportunities to foster this collaboration through CARMA Collaborative and CARMA Support Services. CARMA Collaborative is growing a community of users, prospective users, and other stakeholders who work toward the shared goal of advancing CDA through targeted communications and outreach activities. Deployers of CARMA tools can utilize the CARMA Support Services online help desk service to obtain troubleshooting support for their CDA research.

Looking Ahead

As one of the first tests of CDA technologies in which the vehicles were in complete control of lateral and longitudinal movement, the basic travel use case has set the framework for future research and testing of CDA applications for reducing recurring congestion. Similarly, results from RWM, TIM, and WZM use case testing have contributed to a growing body of research that aims to apply CDA to reduce nonrecurring congestion.

The introduction of CDA has the potential to transform existing and future transportation systems, improving transportation efficiency and safety. Closed track validation tests of use cases under the CDA Reliability and CDA Traffic research tracks are a step in the direction of real-world testing of CDA technologies, with the ultimate goal of technology deployment.

“The CARMA Ecosystem is setting the foundation for the future of open-source technology that advances our understanding of the role of infrastructure in supporting and enabling cooperative automation–an effort we believe strongly supports us in preparing our transportation system for the future,” says Gregory Slater, former Maryland Secretary of Transportation.

Pavle Bujanović is a technical manager in FHWA’s Office of Safety and Operations Research and Development, managing various CDA research projects. He earned a B.S. in civil engineering from Syracuse University, an M.S. in sustainable design and construction from Stanford University, and a Ph.D. in transportation engineering from the University of Texas at Austin.

Steven Vu is a contracted communications specialist in FHWA’s Saxton Transportation Operations Laboratory, contributing to marketing and outreach activities. He earned his B.A. degree from the University of Virginia.

For more information on the CDA Program, visit