Research on Optimization of Intelligent Vehicle Channel Allocation Based on Game Theory in Intersection Scene

In recent years, intelligent transportation systems (ITS) and vehicle to vehicle (V2X) communication have experienced rapid development. For example, Cisco released that the global mobile network traffic showed a growth by 74% in 2016 compared with that of the previous year. The sharp increase in the number of users has intensified the demand for efficient spectrum resource allocation in high-density urban traffic scenarios, which is evident at urban intersections. Recent studies, such as, highlights the challenges currently faced by intelligent driving systems. Traditional static channel allocation methods may not be able to meet the dynamic vehicle distribution and real-time communication requirements at this time, resulting in suboptimal resource allocation, increased latency, and potential security risks. To further mitigate the aforementioned risks, this study proposes a dynamic channel allocation algorithm based on cooperative game theory and simulates it in urban intersection scenarios. This algorithm reduces latency by dynamically allocating resource blocks (RBs), optimizing system throughput while implementing fairness constraints and interference control. This study utilized a joint simulation of Prescan and MATLAB, and the framework was rigorously tested under different vehicle densities (5-25 vehicles). The results show that compared with traditional static methods, the dynamic channel allocation algorithm based on cooperative game theory exhibits significantly reduced latency, and the fairness index remains above 0.8. The algorithm greatly alleviates the "Matthew effect" in resource allocation by integrating real-time vehicle behavior adjustment and resource monopoly punishment mechanisms, ensuring fair access for edge vehicles. This work not only provides a theoretical basis for dynamic spectrum management in vehicle networks, but also offers actionable insights for deploying intelligent transportation systems in urban environments. Efficient communication is crucial for collision avoidance, traffic coordination, and autonomous driving in urban environments. The future directions of this research include applying reinforcement learning to achieve adaptive optimization, and extending the framework to multi base station scenarios to support large-scale smart city applications.