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[2602.18740] HONEST-CAV: Hierarchical Optimization of Network Signals and Trajectories for Connected and Automated Vehicles with Multi-Agent Reinforcement Learning
Summary
The paper presents HONEST-CAV, a hierarchical framework for optimizing traffic flow in mixed environments of human-driven and automated vehicles using multi-agent reinforcement learning.
Why It Matters
As urban areas face increasing traffic congestion and environmental concerns, optimizing traffic management systems is crucial. This study highlights the potential of advanced machine learning techniques to enhance traffic efficiency and reduce energy consumption, making it relevant for urban planners and transportation engineers.
Key Takeaways
- HONEST-CAV optimizes both vehicle-level behaviors and traffic signals.
- The framework uses a decentralized multi-agent reinforcement learning approach.
- Experimental results show significant improvements in speed, fuel consumption, and idling time.
- The method outperforms traditional traffic signal control models.
- CAV proportions significantly influence the performance outcomes of the proposed system.
Computer Science > Machine Learning arXiv:2602.18740 (cs) [Submitted on 21 Feb 2026] Title:HONEST-CAV: Hierarchical Optimization of Network Signals and Trajectories for Connected and Automated Vehicles with Multi-Agent Reinforcement Learning Authors:Ziyan Zhang, Changxin Wan, Peng Hao, Kanok Boriboonsomsin, Matthew J. Barth, Yongkang Liu, Seyhan Ucar, Guoyuan Wu View a PDF of the paper titled HONEST-CAV: Hierarchical Optimization of Network Signals and Trajectories for Connected and Automated Vehicles with Multi-Agent Reinforcement Learning, by Ziyan Zhang and 6 other authors View PDF HTML (experimental) Abstract:This study presents a hierarchical, network-level traffic flow control framework for mixed traffic consisting of Human-driven Vehicles (HVs), Connected and Automated Vehicles (CAVs). The framework jointly optimizes vehicle-level eco-driving behaviors and intersection-level traffic signal control to enhance overall network efficiency and decrease energy consumption. A decentralized Multi-Agent Reinforcement Learning (MARL) approach by Value Decomposition Network (VDN) manages cycle-based traffic signal control (TSC) at intersections, while an innovative Signal Phase and Timing (SPaT) prediction method integrates a Machine Learning-based Trajectory Planning Algorithm (MLTPA) to guide CAVs in executing Eco-Approach and Departure (EAD) maneuvers. The framework is evaluated across varying CAV proportions and powertrain types to assess its effects on mobility and energy...