Edge Computing-Based Contributed Perception and Autonomous Vehicle Groups in Open Scenes

Most existing research on autonomous vehicle groups focuses on utilizing networks to achieve semi-centralized control of leaders/sub-leaders. However, these approaches encounter difficulties when striving to attain precise cooperative environmental awareness in open scenes with inherent interference. To address this problem, we propose a systematic model for distributed autonomous vehicle groups. It incorporates contributed perception among autonomous vehicles. Firstly, this work leverages edge computing to enable a cooperative interaction among autonomous vehicles, thus improving precision of environmental awareness when individual sensing is limited. Then, it introduces a transformer-based prediction method to analyze influencing factors of contributed perception. Finally, it constructs an autonomous vehicle group model and solves it by using a multi-objective optimization method. The simulation results demonstrate that the proposed prediction method has lower mean-square error than existing prediction methods, and the proposed autonomous vehicle group model outperforms existing ones in terms of average group contribution, accessibility, persistence, and timeliness.