QoS-Aware Tripartite Evolutionary Game Strategy: A Task-Driven Performance Optimization Based on ISCC for IoV

In the development of Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV), traditional management strategies that separate communication, sensing, and computation resources, as well as the uneven scheduling of resources, are increasingly unable to meet the needs of complex scenarios such as higher-level autonomous driving and integrated vehicle-road-cloud systems. This paper proposes a Quality of Service (QoS) tripartite evolutionary game model based on task-driven Integrated sensing, communication, and computation (ISCC). The dynamic evolution of resource allocation is analyzed by constructing a resource game platform centered on task requirements. Additionally, a joint utility function involving multi-node competition, dependency, and cooperation as QoS indicators has also been developed to achieve a unified representation of performance metrics. Subsequently, task-driven analysis effectively guides resource allocation strategies, ensuring optimization of resource utilization and precise alignment with task requirements. This paper offers new methodological support for designing and optimizing future intelligent transportation systems.