CIRCLES: Congestion Impact Reduction via CAV-in-the-loop Lagrangian Energy Smoothing

The CIRCLES Website contains more detailed information on this project. 

The Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) project aims to reduce instabilities in traffic flow, called "phantom jams," that cause congestion and wasted energy. If you have ever encountered a temporary traffic jam for no apparent reason, this might have been a phantom jam that occurred naturally because of human driving behavior.


Prior work on closed-course testing demonstrated that phantom jams can be reduced using autonomous vehicle technologies and specially-designed algorithms: but only in a single lane of traffic on a closed course. The CIRCLES project seeks to extend this technology to real-world traffic, where reducing these negative traffic effects could provide ≥10% energy savings.


The ambitious goals of the CIRCLES project will be achieved through a combination of computational development, vehicle technology deployment, and highway infrastructure construction. Novel research is being conducted in all of these areas by an interdisciplinary team across the United States.

Specific major research tasks include: 

  1. Develop high-fidelity simulation tools that exhibit realistic traffic instabilities. 
  2. Discover new techniques in multi-agent reinforcement learning. 
  3. Develop control algorithms to transfer reinforcement learning policies to connected and autonomous vehicles. 
  4. Develop, calibrate, and validate energy models for vehicles. 
  5. Combine vehicle sensing, control, and communication technologies for traffic flow experiments. 
  6. Build highway sensing infrastructure to measure traffic flow impacts by observing the position of every vehicle on the road.

Our team is uniquely capable to provide the results of the work, thanks to collaborations with I-24 MOTION which can measure the impact of traffic while our vehicles are deployed. 


In November 2022, we conducted the largest open-road Advanced Driver Assistance Systems test in history. News articles that cover the test can be found at

Award Number
CID DE-EE0008872
Lead PI
Dan Work
Craig Philip, Jonathan Sprinkle