A Spatial-Temporal Deep Reinforcement Learning Model for Large-Scale Centralized Traffic Signal Control

Yi, Chenglin; Wu, Jia; Ren, Yanyu; Ran, Yunchuan; Lou, Yican

doi:10.1109/itsc55140.2022.9922459

Cited by 2 publications

(1 citation statement)

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“…Results showed that the GAT-DQN performed the best among the baselines and all designed methods. In [61], a GRL-based approach was proposed for traffic signal control. An Actor-Critic framework was utilized, and a GAT model was implemented into the critic network to learn the spatial feature of the surrounding intersection.…”

Section: Attention-based Methodsmentioning

confidence: 99%

Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends

Liu,

Li,

Tang

et al. 2023

Sensors

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The proper functioning of connected and autonomous vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic, including both CAVs and human-driven vehicles. Thus, collaborative decision-making technology for CAVs is essential to generate appropriate driving behaviors to enhance the safety and efficiency of mixed autonomy traffic. In recent years, deep reinforcement learning (DRL) methods have become an efficient way in solving decision-making problems. However, with the development of computing technology, graph reinforcement learning (GRL) methods have gradually demonstrated the large potential to further improve the decision-making performance of CAVs, especially in the area of accurately representing the mutual effects of vehicles and modeling dynamic traffic environments. To facilitate the development of GRL-based methods for autonomous driving, this paper proposes a review of GRL-based methods for the decision-making technologies of CAVs. Firstly, a generic GRL framework is proposed in the beginning to gain an overall understanding of the decision-making technology. Then, the GRL-based decision-making technologies are reviewed from the perspective of the construction methods of mixed autonomy traffic, methods for graph representation of the driving environment, and related works about graph neural networks (GNN) and DRL in the field of decision-making for autonomous driving. Moreover, validation methods are summarized to provide an efficient way to verify the performance of decision-making methods. Finally, challenges and future research directions of GRL-based decision-making methods are summarized.

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Section: Attention-based Methodsmentioning

confidence: 99%