Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs

no code implementations • 26 Feb 2024 • 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 Lichtlé, 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 Zachár, 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, Alexandre M. Bayen

The upper layer is called Speed Planner, and is a centralized optimal control algorithm.

Model Predictive Control

Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed Autonomy Traffic

no code implementations • 26 Feb 2024 • Han Wang, Zhe Fu, Jonathan Lee, Hossein Nick Zinat Matin, Arwa Alanqary, Daniel Urieli, Sharon Hornstein, Abdul Rahman Kreidieh, Raphael Chekroun, William Barbour, William A. Richardson, Dan Work, Benedetto Piccoli, Benjamin Seibold, Jonathan Sprinkle, Alexandre M. Bayen, Maria Laura Delle Monache

The Speed Planner comprises two modules: a TSE enhancement module and a target speed design module.

Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction

1 code implementation • 3 Dec 2021 • Yulin Zhang, William Macke, Jiaxun Cui, Daniel Urieli, Peter Stone

This article establishes for the first time that a multiagent driving policy can be trained in such a way that it generalizes to different traffic flows, AV penetration, and road geometries, including on multi-lane roads.

Autonomous Vehicles

Scalable Multiagent Driving Policies For Reducing Traffic Congestion

1 code implementation • 26 Feb 2021 • Jiaxun Cui, William Macke, Harel Yedidsion, Daniel Urieli, Peter Stone

Next, we propose a modular transfer reinforcement learning approach, and use it to scale up a multiagent driving policy to outperform human-like traffic and existing approaches in a simulated realistic scenario, which is an order of magnitude larger than past scenarios (hundreds instead of tens of vehicles).

Transfer Reinforcement Learning