一句话：通过实证研究识别 V2X 协同感知的六类错误模式，并评估其性能、鲁棒性与驾驶违规风险。
本文研究 V2X 协同感知系统在自动驾驶中的可靠性。此类系统由异构传感器、多个协同代理、不同融合方案与通信条件组成，复杂组合带来诸多运行挑战，且其错误类型与根因尚缺乏系统探索。作者开展大规模实证研究，识别并分析六种常见错误模式，系统评估关键组件对自车感知性能的影响。结果显示：基于 LiDAR 的协同配置感知性能最高；V2I 与 V2V 在不同融合方案下表现不同；协同感知错误增多可能提高驾驶违规频率；系统在线运行时对通信干扰不鲁棒。研究揭示了协同感知关键组件的风险与脆弱性，可指导系统设计与修复。
Perceiving the complex driving environment precisely is crucial to the safe operation of autonomous vehicles. With the tremendous advancement of deep learning and communication technology, Vehicle-to-Everything (V2X) cooperative perception has the potential to address limitations in sensing distant objects and occlusion for a single-agent perception system. V2X cooperative perception systems are software systems characterized by diverse sensor types and cooperative agents, varying fusion schemes, and operation under different communication conditions. Therefore, their complex composition gives rise to numerous operational challenges. Furthermore, when cooperative perception systems produce erroneous predictions, the types of errors and their underlying causes remain insufficiently explored.To bridge this gap, we take an initial step by conducting an empirical study of V2X cooperative perception. To systematically evaluate the impact of cooperative perception on the ego vehicle’s perception performance, we identify and analyze six prevalent error patterns in cooperative perception systems. We further conduct a systematic evaluation of the critical components of these systems through our large-scale study and identify the following key findings: (1) The LiDAR-based cooperation configuration exhibits the highest perception performance; (2) Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication exhibit distinct cooperative perception performance under different fusion schemes; (3) Increased cooperative perception errors may result in a higher frequency of driving violations; (4) Cooperative perception systems are not robust against communication interference when running online. Our results reveal potential risks and vulnerabilities in critical components of cooperative perception systems. We hope that our findings can better promote the design and repair of cooperative perception systems.
DOI 原文 ·
@inproceedings{paperbot9305,
title = {When Autonomous Vehicle Meets V2X Cooperative Perception: How Far Are We?},
author = {An Guo and Shuoxiao Zhang and Enyi Tang and Xinyu Gao and Haomin Pang and Haoxiang Tian and Yanzhou Mu and Wu Wen and Chunrong Fang and Zhenyu Chen},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00101}
}