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

Abstract: 

This article presents a novel hierarchical speed planning framework for variable speed limits in mixed-autonomy traffic environments, leveraging server-side macroscopic control and vehicle-side microscopic execution. The framework integrates real-time traffic state estimation (TSE) and reinforcement learning (RL)-based control to mitigate congestion and improve traffic flow. A TSE enhancement module combines macroscopic data from sources like INRIX with high-resolution observations from connected autonomous vehicles (CAVs), enabling predictive modeling to address latency and noise. The target speed design module employs kernel smoothing and a buffer zone strategy to optimize traffic density and flow around bottlenecks. The proposed system was validated in the largest open-road test to date with 100 CAVs, demonstrating an overall 8% traffic density decrease, with a specific decrease of 7% upstream, 10% downstream, and a 52% decrease during the congestion formation phase at bottlenecks.

Author: 
Wang, Han
Fu, Zhe
Lee, Jonathan W.
Matin, Hossein Nick Zinat
Alanqary, Arwa
Urieli, Daniel
Hornstein, Sharon
Kreidieh, Abdul Rahman
Chekroun, Raphael
Barbour, William
Richardson, William A.
Work, Dan
Piccoli, Benedetto
Seibold, Benjamin
Sprinkle, Jonathan M.
Bayen, Alexandre M.
Monache, Maria Laura Delle
Publication date: 
February 1, 2025
Publication type: 
Journal Article
Citation: 
Wang, H., Fu, Z., Lee, J. W., Matin, H. N. Z., Alanqary, A., Urieli, D., Hornstein, S., Kreidieh, A. R., Chekroun, R., Barbour, W., Richardson, W. A., Work, D., Piccoli, B., Seibold, B., Sprinkle, J., Bayen, A. M., & Monache, M. L. D. (2025). Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed-Autonomy Traffic. IEEE Control Systems, 45(1), 111–138. IEEE Control Systems. https://doi.org/10.1109/MCS.2024.3499212