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

Wang, Han [University of California, Berkeley, CA (United States); University of California Berkeley] (ORCID:0000000315617532); Fu, Zhe [University of California, Berkeley, CA (United States)] (ORCID:0000000344783978); Lee, Jonathan W. [University of California, Berkeley, CA (United States)] (ORCID:000000033932761X); Matin, Hossein Nick Zinat [University of California, Berkeley, CA (United States)] (ORCID:000000024383369X); Alanqary, Arwa [University of California, Berkeley, CA (United States)]; Urieli, Daniel [General Motors, Herzliya (Israel)] (ORCID:000900028484945X); Hornstein, Sharon [General Motors R&D, Herzliya (Israel)]; Kreidieh, Abdul Rahman [University of California, Berkeley, CA (United States)]; Chekroun, Raphael [University of California, Berkeley, CA (United States); Valeo, Paris (France); Mines Paris - PSL University, Paris (France)]; Barbour, William [Vanderbilt University, Nashville, TN (United States)]; Richardson, William A. [Vanderbilt University, Nashville, TN (United States)]; Work, Dan [Vanderbilt University, Nashville, TN (United States)]; Piccoli, Benedetto [Rutgers University, Camden, NJ (United States)]; Seibold, Benjamin [Temple University, Philadelphia, PA (United States)]; Sprinkle, Jonathan [Vanderbilt University, Nashville, TN (United States)] (ORCID:0000000341761212); Bayen, Alexandre M. [University of California, Berkeley, CA (United States); Google Research, Mountain View, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)]; Monache, Maria Delle [University of California, Berkeley, CA (United States)]

doi:10.1109/mcs.2024.3499212

DOE OSTI · 2545917

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

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Abstract

Here, 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.

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32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION 42 ENGINEERING 54 ENVIRONMENTAL SCIENCES Autonomous vehicles Lagrangian functions Path planning Predictive models Probes Real-time systems Reinforcement learning Robustness Smoothing methods State estimation System analysis and design Variable speed drives

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Wang, Han [University of California, Berkeley, CA (United States); University of California Berkeley] (ORCID:0000000315617532), Fu, Zhe [University of California, Berkeley, CA (United States)] (ORCID:0000000344783978), Lee, Jonathan W. [University of California, Berkeley, CA (United States)] (ORCID:000000033932761X), Matin, Hossein Nick Zinat [University of California, Berkeley, CA (United States)] (ORCID:000000024383369X), Alanqary, Arwa [University of California, Berkeley, CA (United States)], Urieli, Daniel [General Motors, Herzliya (Israel)] (ORCID:000900028484945X), Hornstein, Sharon [General Motors R&D, Herzliya (Israel)], Kreidieh, Abdul Rahman [University of California, Berkeley, CA (United States)], Chekroun, Raphael [University of California, Berkeley, CA (United States); Valeo, Paris (France); Mines Paris - PSL University, Paris (France)], Barbour, William [Vanderbilt University, Nashville, TN (United States)], Richardson, William A. [Vanderbilt University, Nashville, TN (United States)], Work, Dan [Vanderbilt University, Nashville, TN (United States)], Piccoli, Benedetto [Rutgers University, Camden, NJ (United States)], Seibold, Benjamin [Temple University, Philadelphia, PA (United States)], Sprinkle, Jonathan [Vanderbilt University, Nashville, TN (United States)] (ORCID:0000000341761212), Bayen, Alexandre M. [University of California, Berkeley, CA (United States); Google Research, Mountain View, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)], Monache, Maria Delle [University of California, Berkeley, CA (United States)]. 2025-01-30. Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed-Autonomy Traffic. https://doi.org/10.1109/mcs.2024.3499212

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