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.
Abstract:
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