Multi-Agent Cooperative Perception: A Structural Evaluation Framework for Autonomous Systems

With multi-agent systems developing rapidly in various fields, cooperative perception has attracted much attention as a critical technology to enhance the intelligence level of autonomous systems. However, in the face of actual complex scenes, the perception ability of a single intelligent agent is often constrained by problems such as occlusion and limited perception range. To this end, multi-agent inter-intelligence cooperative sensing has emerged to enhance the overall perception performance by sharing perception information cooperatively. In this paper, we structurally study the multi-agent cooperative perception in depth and propose a novel and comprehensive evaluation framework to address the current situation in existing literature, which mainly focuses on latency or communication factors in cooperative perception evaluation. The framework covers four key modules: feature extraction, feature compression, feature fusion, and target detection, and aims to address the multifaceted challenges in multi-agent perception. Through an in-depth evaluation of existing cooperative perception algorithms, we comprehensively map the performance of each algorithm under the guidance of the framework. Particularly, we find that in the absence of aligning the correct pose, the detection performance degrades drastically as the latency increases. Our comprehensive framework will drive the development of multi-agent cooperative perception by providing researchers with a transparent and standardised methodology for evaluating, comparing, and improving existing cooperative perception approaches.