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Uni-Light: A unified deep reinforcement learning framework for cooperative traffic signal and connected and automated vehicles control
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DOI：10.1016/j.ijtst.2026.01.002.png)
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Abstract
En 中文
In recent years, deep reinforcement learning (DRL) has been widely applied to urban traffic signal control in mixed traffic environments where human-driven vehicles (HDVs) and connected and automated vehicles (CAVs) coexist. Most existing studies optimize signal timing based on mixed traffic flow characteristics but overlook CAVs’ control strategies. To achieve cooperative control, some approaches design both traffic signals and CAVs as agents within a multi-agent reinforcement learning framework. However, such methods often suffer from high model complexity and computational costs, making real-world deployment challenging. To address these issues, we propose a novel unified control framework, Uni-Light, which introduces an invalid-action masking mechanism within a DRL-based multidimensional action space. This design constrains frequent signal phase switching while filtering out infeasible vehicle actions under red phases, enabling Uni-Light to generate both low-frequency signal control and high-frequency CAV speed control with only the traffic signal as an agent. The CAV control is applied exclusively to the leading CAV in each approach lane, resulting in a simplified yet efficient cooperative strategy. Moreover, a grid-based microscopic state representation is fused with macroscopic traffic features to enhance the agent’s perception capability. Simulation results demonstrate that Uni-Light significantly improves network throughput and reduces energy consumption compared with existing methods, while maintaining stability and ensuring safe vehicle operations under varying CAV penetration rates. This study provides a high-performance and novel paradigm for cooperative control between traffic signals and CAVs, further facilitating the deployment of cooperative control strategies in mixed traffic and enhancing the operational safety of autonomous vehicles.
Keywords:
Deep reinforcement learning (DRL)
Traffic signal control
Cooperative control
Connected and automated vehicles (CAVs)
Transportation science and technology
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