Low Latency Multi - Dimensional Offloading in Intelligent Transportation System

Abstract

Artificial intelligence will play a vital role for autonomous and cooperative driving in future intelligent transportation systems (ITS). Whereas, it needs sufficient computing resource, data resource and elaborated models to provide powerful artificial intelligence for enhanced driving and cooperating in ITS. Current schemes, such as computation offloading, distributed data caching, and remote model training, usually focus on part of this problem, and cannot substantially solve the multidimensional burden in such intelligent system. In this paper, we propose a multi-dimensional offloading (MDO) scheme to realize efficient and joint offloading of computation, data cache and decision model in ITS. Specifically, an optimization model of MDO is presented and divided into two sub-problems to get the optimized resource allocation with extremely low latency, which is crucial for a time-sensitive system. Simulations are conducted to verify the performance of our proposed scheme, and the simulation results show MDO can decrease the time latency considerably compared with traditional schemes.