Multi-Agent Cooperative Control of CAVs in Toll Plaza Diverging Areas: A Target-Path Approach

Existing research on cooperative control of connected and autonomous vehicles (CAVs) has primarily focused on structured freeway environments. Most existing approaches adopt lane-based modeling and discrete lane-change actions. These assumptions are unsuitable for toll plaza diverging areas without lane markings, where vehicles move toward multiple tollbooths. The absence of predefined lanes leads to continuous trajectory evolution, dense interactions, and increased safety risk. To address this limitation, this study proposes a multi-agent cooperative control framework based on Multi-Agent Proximal Policy Optimization (MAPPO) under a Centralized Training and Decentralized Execution (CTDE) architecture. The multi-agent formulation captures multi-vehicle interaction in toll plaza diverging areas, while centralized training improves learning stability. A target-path-oriented action space is introduced to replace the discrete lane-change action, enabling flexible tollbooth selection and continuous trajectory generation. The proposed cooperative strategy is trained and evaluated on a simulation platform structured under a Perception-Decision-Action framework, which provides a high-fidelity environment for weak-constraint traffic interactions. Simulation results based on real-world traffic data show that the proposed method improves traffic efficiency and enhances collision avoidance. Furthermore, comparative analyses are conducted to evaluate the model performance under varying traffic environments.