Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs
<|reference_start|>Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs: The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"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 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 paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called 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 on-board 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 modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest.<|reference_end|>
@article{lee2024traffic,
    title={Traffic Control via Connected and Automated Vehicles: An Open-Road Field
  Experiment with 100 CAVs},
    author={Jonathan W. Lee, Han Wang, Kathy Jang, Amaury Hayat, Matthew Bunting,
  Arwa Alanqary, William Barbour, Zhe Fu, Xiaoqian Gong, George Gunter, Sharon
  Hornstein, Abdul Rahman Kreidieh, Nathan Lichtl'e, Matthew W. Nice, William
  A. Richardson, Adit Shah, Eugene Vinitsky, Fangyu Wu, Shengquan Xiang,
  Sulaiman Almatrudi, Fahd Althukair, Rahul Bhadani, Joy Carpio, Raphael
  Chekroun, Eric Cheng, Maria Teresa Chiri, Fang-Chieh Chou, Ryan Delorenzo,
  Marsalis Gibson, Derek Gloudemans, Anish Gollakota, Junyi Ji, Alexander
  Keimer, Nour Khoudari, Malaika Mahmood, Mikail Mahmood, Hossein Nick Zinat
  Matin, Sean Mcquade, Rabie Ramadan, Daniel Urieli, Xia Wang, Yanbing Wang,
  Rita Xu, Mengsha Yao, Yiling You, Gergely Zach'ar, Yibo Zhao, Mostafa Ameli,
  Mirza Najamuddin Baig, Sarah Bhaskaran, Kenneth Butts, Manasi Gowda, Caroline
  Janssen, John Lee, Liam Pedersen, Riley Wagner, Zimo Zhang, Chang Zhou,
  Daniel B. Work, Benjamin Seibold, Jonathan Sprinkle, Benedetto Piccoli, Maria
  Laura Delle Monache, and Alexandre M. Bayen},
    journal={arXiv preprint arXiv:2402.17043},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17043},
    primaryClass={eess.SY cs.SY}
}