Priority-Based Resource Allocation in Vehicular Communication Using Q-Learning

Vehicular communication has the potential to revolutionize road safety, traffic efficiency, infotainment services, and autonomous driving. However, as urban traffic complexity increases, intelligent transportation solutions are essential. Priority-based resource allocation is crucial for optimizing network performance and information transmission efficiency in the Internet of Vehicles (IoV). In this context, Q- learning optimizes resource allocation in vehicular communication systems, prioritizing real-time traffic conditions. The Q-learning model effectively allocates resources based on priority, continuously improving strategy over time. This study shows the significant potential of the Q-Learning model for optimizing priority-based resource allocation in vehicular communication, enhancing road safety and efficiency.