Research on Cooperative Consensus of Multi-Vehicle Systems Based on Point Cloud and Dynamic Leader

This paper explores the integration of point cloud perception and dynamic leader mechanisms to enhance cooperative consensus among multi-vehicle systems in autonomous driving environments. As autonomous driving technologies evolve, the necessity for multi-vehicle intelligence becomes apparent due to the complexities and variability of traffic environments which single vehicles alone cannot efficiently navigate. This study introduces a method that utilizes point cloud data for creating a unified environmental model that facilitates data sharing among vehicles, thus enabling a dynamic leader election system that optimizes traffic flow and energy consumption. By leveraging the capabilities of point cloud technology to provide a comprehensive view and high-precision spatial semantic information, and integrating it with a dynamic leader mechanism, this approach aims to improve decision-making accuracy and reduce system latency. The fusion of these technologies not only enhances the operational efficiency of individual vehicles but also ensures a cooperative interaction between multiple vehicles, leading to more effective management of complex traffic scenarios. This research potentially paves the way for significant advancements in smart transportation and unmanned logistics, promoting broader implementation of vehicle-road-cloud integration technologies.