HONEST-CAV: Hierarchical Optimization of Network Signals and Trajectories for Connected and Automated Vehicles with Multi-Agent Reinforcement Learning
arXiv:2602.18740v1 Announce Type: new 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 performance. Experimental results conducted in a 4*4 real-world network de
arXiv:2602.18740v1 Announce Type: new 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 performance. Experimental results conducted in a 4*4 real-world network demonstrate that the MARL-based TSC method outperforms the baseline model (i.e., Webster method) in speed, fuel consumption, and idling time. In addition, with MLTPA, HONEST-CAV benefits the traffic system further in energy consumption and idling time. With a 60% CAV proportion, vehicle average speed, fuel consumption, and idling time can be improved/saved by 7.67%, 10.23%, and 45.83% compared with the baseline. Furthermore, discussions on CAV proportions and powertrain types are conducted to quantify the performance of the proposed method with the impact of automation and electrification.
Executive Summary
This study presents HONEST-CAV, a hierarchical framework for optimizing network signals and trajectories of connected and automated vehicles (CAVs). The framework leverages multi-agent reinforcement learning (MARL) and Machine Learning-based Trajectory Planning Algorithm (MLTPA) to enhance network efficiency and reduce energy consumption. Results show that the MARL-based method outperforms the baseline model in speed, fuel consumption, and idling time. Furthermore, the integration of MLTPA leads to improved energy consumption and idling time. The study's findings have significant implications for the development of smart transportation systems and highlight the potential benefits of CAVs in reducing energy consumption and improving traffic flow.
Key Points
- ▸ HONEST-CAV is a hierarchical framework for optimizing network signals and trajectories of CAVs.
- ▸ The framework uses MARL and MLTPA to enhance network efficiency and reduce energy consumption.
- ▸ Results show that the MARL-based method outperforms the baseline model in speed, fuel consumption, and idling time.
Merits
Strength in Mathematical Modeling
The study presents a rigorous mathematical framework for optimizing network signals and trajectories of CAVs, which is a significant contribution to the field of transportation engineering.
Comprehensive Evaluation
The study evaluates the performance of the proposed framework across varying CAV proportions and powertrain types, providing a comprehensive understanding of its benefits and limitations.
Demerits
Limitation in Real-World Implementation
The study's results are based on simulations and may not accurately reflect real-world scenarios, which could limit the practical application of the proposed framework.
Dependence on Data Quality
The performance of the proposed framework relies heavily on the quality and accuracy of the data used for training and evaluation, which could be a limitation in real-world applications.
Expert Commentary
The study presents a significant contribution to the field of transportation engineering, leveraging multi-agent reinforcement learning and Machine Learning-based Trajectory Planning Algorithm to optimize network signals and trajectories of CAVs. While the study's results are promising, further research is needed to address the limitations of real-world implementation and dependence on data quality. The study's findings have significant implications for the development of smart transportation systems and highlight the potential benefits of CAVs in reducing energy consumption and improving traffic flow.
Recommendations
- ✓ Future studies should focus on developing more robust and scalable frameworks for real-world implementation.
- ✓ Researchers should investigate the potential benefits of integrating additional data sources and sensors into the proposed framework.
