Joint Task Offloading and Resource Allocation for Vehicle Platoon: A Nested Algorithm Based on Stackelberg Game

This is the authors' abstract. We don't add key points for this paper.

With the increasing computational demands of intelligent connected vehicles (ICVs), traditional roadside mobile edge computing (MEC) faces resource and coverage limitations. As a mobile computing entity, a vehicle platoon enables coordinated resource scheduling and low-latency communication, serving as an effective supplement to edge computing. However, limited onboard resources make vehicle platoons insufficient for handling heavy task loads. This study focuses on the joint task offloading and resource allocation between vehicle platoons and MEC servers. A MEC-assisted vehicle platoon computing network (MVPCN) is constructed, where both platoon vehicles (PVs) and MEC servers serve as computing nodes. We formulate a joint optimization problem of task offloading and resource allocation in the vehicle platoon, aiming to minimize the long-term weighted cost of time and energy consumption. Given the complexity of the problem and the limitations of conventional solution methods, we design a Discrete Two-Stage Stackelberg Game (DTSG) to decompose the problem into two subproblems: leader (platoon leader) level and follower (computing nodes) level, and prove the existence of a Stackelberg equilibrium (SE). Based on the game model, we propose a Nested Platoon Task Offloading and Resource Allocation (NPTORA) algorithm. The leader employs the multi-agent proximal policy optimization (MAPPO) to generate offloading decisions for the platoon, while the followers allocate computational resources using a Dynamic Priority-based Hybrid Optimization (DPHO) algorithm based on the received offloading decisions. Simulation results show that NPTORA outperforms baseline methods in task success, cost efficiency, and adaptability, demonstrating strong performance and practical potential.