Reducing Power Consumption and Latency of Autonomous Vehicles With Efficient Task and Path Assignment in the V2X-MEC Based on Nash Equilibrium

Nowadays, autonomous vehicles (AVs) have become an excellent solution for alleviating the burden of the traffic system and improving safety. However, the large-scale application of AVs is significantly restrained by the high-power cost of their numerous sensors and processors, which can reach hundreds of watts and occupy most of the vehicular power consumption. To meet such tremendous computation requirements, most AVs rely on vehicle-to-everything (V2X) communication with mobile edge computing (MEC) techniques to relocate their tasks to external computing. In this paper, the problem of optimizing the task and path assignment process in the V2X-MEC system is tackled for further reducing vehicular power consumption with the constraint of transmission latency. We incorporate the Nash Equilibrium (NE) concept for generating the optimal assignment of tasks and paths, and the attractor selection model (ASM) is introduced to flexibly allocate tasks with consideration of different energy-saving requirements. With comprehensive simulation experiments, we show that the proposed method can save approximately 85% energy compared with the method without task assignment, and it is over 9% and 16% better than the shortest path assignment method and the minimum average delay method, respectively. Besides, the power consumption in AVs with low remaining electricity can be further reduced by over 43%.