Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed Autonomy Traffic
<|reference_start|>Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed Autonomy Traffic: This paper introduces a novel control framework for Lagrangian variable speed limits in hybrid traffic flow environments utilizing automated vehicles (AVs). The framework was validated using a fleet of 100 connected automated vehicles as part of the largest coordinated open-road test designed to smooth traffic flow. The framework includes two main components: a high-level controller deployed on the server side, named Speed Planner, and low-level controllers called vehicle controllers deployed on the vehicle side. The Speed Planner designs and updates target speeds for the vehicle controllers based on real-time Traffic State Estimation (TSE) [1]. The Speed Planner comprises two modules: a TSE enhancement module and a target speed design module. The TSE enhancement module is designed to minimize the effects of inherent latency in the received traffic information and to improve the spatial and temporal resolution of the input traffic data. The target speed design module generates target speed profiles with the goal of improving traffic flow. The vehicle controllers are designed to track the target speed meanwhile responding to the surrounding situation. The numerical simulation indicates the performance of the proposed method: the bottleneck throughput has increased by 5.01%, and the speed standard deviation has been reduced by a significant 34.36%. We further showcase an operational study with a description of how the controller was implemented on a field-test with 100 AVs and its comprehensive effects on the traffic flow.<|reference_end|>
@article{wang2024hierarchical,
    title={Hierarchical Speed Planner for Automated Vehicles: A Framework for
  Lagrangian Variable Speed Limit in Mixed Autonomy Traffic},
    author={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, and Maria
  Laura Delle Monache},
    journal={arXiv preprint arXiv:2402.16993},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16993},
    primaryClass={eess.SY cs.SY}
}