When Vehicular Fog Computing Meets Autonomous Driving: Computational Resource Management and Task Offloading
Zhenyu Zhou, Haijun Liao, Xiaoyan Wang, Shahid Mumtaz, Jonathan Rodrı́guez
Abstract
Autonomous driving has the potential to make transportation systems safer, greener and more efficient. To realize autonomous driving, a great deal of in-car cutting-edge applications such as augmented reality, dynamic path planning and cognitive driving systems are required, which need significant computational resources and near realtime response. To cope with this new paradigm, vehicular fog computing (VFC) has emerged recently, which migrates the computing from congested base stations (or cloud servers) to nearby vehicles with under-utilized computational resources. in VFC, the designs of server recruitment and task offloading strategies under information asymmetry and uncertainty pose new technical challenges. in this article, we propose a two-stage VFC framework to address these challenges. The framework consists of a contract theory based vehicular computational resource management mechanism, and a matching-learning based task offloading mechanism. Simulation results demonstrate that the proposed framework can boost the performance of VFC in terms of resource utilization efficiency and task offloading delay.