Evaluation Method of Vehicle-road-cloud Collaborative System with Grey System Theory

In the era of autonomous driving, to truly achieve efficient and safe transportation and travel, the intelligence of a single vehicle is far from enough. With the development of a series of technologies such as 5G, V2X and artificial intelligence, vehicle-road-cloud collaboration is becoming more and more the main application direction in the future. It is imperative to effectively evaluate the safety, effectiveness and service capability of the vehicle-road-cloud collaboration system. Based on this, this study firstly selected five first-level evaluation indexes, namely, execution ability, V2X ability, environment perception and positioning accuracy, application scene function, and comprehensive driving ability, and constructed the evaluation index system of the vehicle-road-cloud collaborative system. Then, on the basis of the multi-level index system, a comprehensive evaluation method of the vehicle-road-cloud collaborative system based on the Grey system is determined. Finally, an empirical study on the comprehensive evaluation of the vehicle-road-cloud cooperative system is carried out. The results show that the evaluation method proposed in this paper can effectively and comprehensively evaluate the vehicle-road-cloud cooperative system.