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

JW Lee, H Wang, K Jang, N Lichtlé… - IEEE Control …, 2025 - ieeexplore.ieee.org
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 …

Reinforcement learning based oscillation dampening: Scaling up single-agent RL algorithms to a 100 AV highway field operational test

K Jang, N Lichtlé, E Vinitsky, A Shah, M Bunting… - arxiv preprint arxiv …, 2024 - arxiv.org
In this article, we explore the technical details of the reinforcement learning (RL) algorithms
that were deployed in the largest field test of automated vehicles designed to smooth traffic …

On the analytical properties of a nonlinear microscopic dynamical model for connected and automated vehicles

HNZ Matin, Y Yeo, X Gong… - IEEE Control Systems …, 2024 - ieeexplore.ieee.org
In this letter, we propose an integrated dynamical model of Connected and Automated
Vehicles (CAVs) which incorporates CAV technologies and a microscopic car-following …

Design, preparation, and execution of the 100-AV field test for the CIRCLES consortium: Methodology and implementation of the largest mobile traffic control …

M Ameli, ST Mcquade, JW Lee, M Bunting… - IEEE Control …, 2025 - ieeexplore.ieee.org
This article presents the comprehensive design, setup, execution, and evaluation of the
MegaVanderTest (MVT) experiment conducted by the Congestion Impacts Reduction via …

Kernel-based planning and imitation learning control for flow smoothing in mixed autonomy traffic

Z Fu, A Alanqary, AR Kreidieh, AM Bayen - Transportation Research Part C …, 2024 - Elsevier
This article presents a new architecture for managing heterogeneous fleets aimed at
achieving flow harmonization in mixed-autonomy traffic, demonstrating robustness across …

Reinforcement learning-based oscillation dampening: Scaling up single-agent reinforcement learning algorithms to a 100-autonomous-vehicle highway field …

K Jang, N Lichtlé, E Vinitsky, A Shah… - IEEE Control …, 2025 - ieeexplore.ieee.org
In this article, we explore the technical details of the reinforcement learning (RL) algorithms
that were deployed in the largest field test of automated vehicles designed to smooth traffic …

CIRCLES: Congestion impacts reduction via CAV-in-the-loop Lagrangian energy smoothing

AM Bayen, JW Lee, B Piccoli, B Seibold, JM Sprinkle… - 2024 - osti.gov
The energy efficiency of today's vehicular mobility relies on the un-integrated combination of
i) control via static assets (traffic lights, metering, variable speed limits, etc.); and ii) onboard …

Cooperative Cruising: Reinforcement Learning based Time-Headway Control for Increased Traffic Efficiency

Y Veksler, S Hornstein, H Wang, MLD Monache… - arxiv preprint arxiv …, 2024 - arxiv.org
The proliferation of Connected Automated Vehicles represents an unprecedented
opportunity for improving driving efficiency and alleviating traffic congestion. However …

Mesoscale Traffic Forecasting for Real-Time Bottleneck and Shockwave Prediction

R Chekroun, H Wang, J Lee, M Toromanoff… - arxiv preprint arxiv …, 2024 - arxiv.org
Accurate real-time traffic state forecasting plays a pivotal role in traffic control research. In
particular, the CIRCLES consortium project necessitates predictive techniques to mitigate …