Traffic Control via Connected and Automated Vehicles (CAVs): An Open-Road Field Experiment with 100 CAVs
Journal Article
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· IEEE Control Systems
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- University of California, Berkeley, CA (United States). et al.
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
- Waymo, San Francisco, CA, USA
- Ecole des Ponts Paristech, Champs sur Marne, France
- Institute for Software Integrated Systems, Vanderbilt University, TN, USA
- University of California, Berkeley, Berkeley, CA, USA
- Amherst College, Amherst, MA, USA
- General Motors R&D, Herzliya, Israel
- Google, Mountain View, CA, USA
- Virginia Tech Transportation Institute, Blacksburg, VA, USA
- New York University, New York City, NY, USA
- Cornell University, Ithaca, NY, USA
- Peking University, Beijing, China
- University of Alabama, Huntsville, Huntsville, AL, USA
- Nissan Advanced Technology Center, Santa Clara, CA, USA
- Footovision, Paris, France
- Unity Technologies, San Francisco, CA, USA
- Queen’s College, Kingston, ON, Canada
- Rutgers University—Camden, Camden, NJ, USA
- Department of Computer Science, Institute for Software Integrated Systems, Vanderbilt University, TN, USA
- FAU Erlan-gen-Nürnberg, Erlangen, Germany
- Purdue University, West Lafayette, IN, USA
- Rutgers University - Camden, Camden, NJ, USA
- Temple University, Philadelphia, PA, USA
- Legal & General America, Frederick, MD, USA
- General Motors, R&D, Herzliya, Israel
- Meta, Menlo Park, CA, USA
- Northeastern University, Boston, MA, USA
- Transportation Engineering and Computer Science Lab (GRETTIA), University Gustave Eiffel Paris, Champs-sur-Marne, France
- Duolingo, Pittsburgh, PA, USA
- Toyota Motor Engineering and Manufacturing North America, USA
- Meta, Seattle, WA, USA
- Vanderbilt University, Nashville, TN, USA
- Wing, Palo Alto, CA, USA
- Amazon, Seattle, WA, USA
- Amazon Web Services, East Palo Alto, CA, USA
- Rutgers University — Camden, Camden, NJ, USA
- Vanderbilt University, TN, USA
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA, USA
The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. Also called “phantom jams” or “stop-and-go waves,” these instabilities are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system, referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment, the MegaVanderTest (MVT), leveraged a heterogeneous fleet of 100 longitudinally controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this article. The MegaController is a hierarchical control architecture that consists of two main layers. The upper layer is called the Speed Planner and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock onboard sensors. The Speed Planner ingests live data feeds provided by third parties as well as data from our own control vehicles and uses both to perform the speed assignment. The architecture of the Speed Planner allows for the modular use of standard control techniques, such as optimal control, model predictive control (MPC), kernel methods, and others. The architecture of the local controller allows for the flexible implementation of local controllers. Corresponding techniques include deep reinforcement learning (RL), MPC, and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers or only some. Likewise, control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars to electronic selection of adaptive cruise control (ACC) setpoints in others. The proposed architecture technically allows for the combination of all possible settings proposed previously, that is {Speed Planner algorithms} × {local Vehicle Controller algorithms} × {full or partial sensing} × {torque or speed control}. As a result, most configurations were tested throughout the ramp up to the MegaVandertest (MVT).
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Rutgers University-Camden, NJ (United States); Temple University, Philadelphia, PA (United States); University of California, Berkeley, CA (United States); Vanderbilt University, Nashville, TN (United States)
- Sponsoring Organization:
- National Science Foundation (NSF); US Department of Energy; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO) (EE-3V)
- Grant/Contract Number:
- AC02-05CH11231; EE0008872
- OSTI ID:
- 2545911
- Alternate ID(s):
- OSTI ID: 2572106
- Journal Information:
- IEEE Control Systems, Journal Name: IEEE Control Systems Journal Issue: 1 Vol. 45; ISSN 1066-033X
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
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