Contributed Perception-Based Dynamic Evolution Method for Autonomous Vehicle Groups in Open Scenes

Qichao Mao, Jiujun Cheng, MengChu Zhou, Zhangkai Ni, Guiyuan Yuan, Shangce Gao, Chuanhuang Li

Abstract: Accurately handling dynamic evolution events is a significant challenge for autonomous vehicle groups (AVGs) in open scenes, which can be affected by complex road conditions and various interference factors. Existing work on the dynamic evolution of AVGs in open scenes concentrates on semi-centralized groups, assessing communication links as the sole criterion. However, there lack the mathematical analysis of and methods for the dynamic evolution of events in distributed AVGs with cooperative perception. To address this issue, we propose a contributed perception-based dynamic evolution method designed for distributed AVGs. This method ensures that group members can continuously and timely exchange valid perceptual information. First, we investigate the impact of external interference on the contributed perception of vehicle groups to understand the drivers behind their dynamic evolution. Second, we define a range of vehicle group evolution behaviors and corresponding handling methods in response to external interference. Lastly, we introduce group states and perceptibility to delineate the evolution dynamics. Simulation results demonstrate the superiority of our proposed method over existing ones in terms of average group contribution, accessibility, persistence, timeliness, and perceptibility.

External IDs:dblp:journals/tmc/MaoCZNYGL25