A V2X-Based Multi-Agent Framework for Cooperative Path Planning and Dynamic Decision-Making in Autonomous Driving
DOI:
https://doi.org/10.53469/wjimt.2025.08(05).10Keywords:
V2X communication, Multi-agent system, Path planning, Graph neural network, Traffic coordinationAbstract
In complex and heterogeneous traffic environments, traditional single-vehicle planning strategies fail to meet the global coordination demands of vehicle-to-vehicle and vehicle-to-infrastructure interactions. This study proposes a multi-agent cooperative path planning and decision-making framework based on V2X communication technology. A coordinated architecture consisting of a central controller and edge-level vehicle nodes is constructed. Graph Neural Networks (GNNs) are employed to model the dynamic states of nearby vehicles, and a multi-stage joint optimization algorithm is introduced to simultaneously optimize path efficiency and conflict avoidance. The framework is evaluated on the SUMO real-world traffic simulation platform and the Apollo real-vehicle testing platform. Experimental results show that the system significantly improves traffic throughput (+28.6%) and reduces path conflict rates (−35.4%) in high-interaction areas such as intersections and merging zones, demonstrating strong adaptability to coordinated traffic scenarios.
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