HierNet: A Hierarchical Resource Allocation Method for Vehicle Platooning Networks

Vehicle platooning is a promising traffic model in intelligent transportation systems (ITS), which can effectively improve resource utilization and reduce traffic congestion. The resource allocation for vehicle-to-everything (V2X) communications that consist of intra-platoon communications and inter-platoon communications is crucial for safe operation of multiple vehicular platoons. Considering dynamic coordination pattern of vehicular platoons and layered architecture of vehicle platooning networks, a hierarchical resource decision-making framework is proposed in this paper. In the proposed framework, the resource decision-making process is divided into two levels. The high level that generates and distributes coordination meta policy is deployed on base station (BS), and the low level that generates ego resource decisions is deployed in each platoon. To deal with optimization of resource allocation for multi-platoon V2X communications, a hierarchical reinforcement learning method (HierNet) is designed based on the proposed hierarchical decision-making framework. In HierNet, meta policy of the high level can be preserved, and needs to be updated only when cooperative conditions of multiple platoons undergo distinct changes. Simulation experiments have demonstrated that our proposed method not only optimizes resource efficiency, but also reduces the communication costs for resource decision-making of vehicle platooning networks.