Dynamic Load Balancing Framework for Compute-Network Resource Integration in MEC-Assisted Autonomous Vehicles

As autonomous driving technology becomes more advanced, vehicle-edge computing (VEC) has drawn significant attention. However, it still faces challenges due to varying network conditions and the availability of roadside units (RSUs). In this paper, we present a Lyapunov optimization-based algorithm that jointly optimizes offloading decisions and computing resources, aiming to reduce energy consumption while keeping service time within acceptable limits through both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. We then evaluate the real-world performance of this algorithm by using simulator, which integrates a network model, an in-vehicle processing model in MATLAB, a vehicle topology model, and realistic driving scenarios generated with a virtual test drive (VTD).