An Integrated Network-Computing Load Balancing Simulator for VEC-Assisted Autonomous Vehicles

Abstract

Achievement of offloaded analytics services through vehicle edge computing (VEC) requires a comprehensive analysis of in-vehicle processing and network environments. However, existing research on autonomous driving technologies leveraging VEC and related simulation studies remains in its early stages. This article presents the development of an integrated network-computing load (INCL) balancing simulator for autonomous vehicles, which combines a network model and an in-vehicle processing model implemented in MATLAB with a vehicle topology model and realistic driving scenarios created using a virtual test drive (VTD). Moreover, eight real-world autonomous driving scenarios are proposed to validate the simulator's performance, demonstrating its ability to effectively balance network and computational loads under diverse conditions. Finally, using a case study in a platooning driving scenario, we evaluate the simulator's capability to optimize resource utilization, paving the way for advanced autonomous driving technologies.