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.

翻译：随着深度学习和通信技术的巨大进步，车联网（V2X）协同感知有望解决单智能体感知系统在远距离物体感知和遮挡方面的局限性。V2X协同感知系统是软件系统，其特点包括多样的传感器类型与协同智能体、不同的融合方案以及在各种通信条件下的运行。因此，其复杂的构成带来了诸多运行挑战。此外，当协同感知系统产生错误预测时，其错误类型及其根本原因仍未得到充分探索。为弥合这一差距，我们迈出了第一步，对V2X协同感知进行了实证研究。为系统评估协同感知对主车感知性能的影响，我们识别并分析了协同感知系统中六种常见的错误模式。我们进一步通过大规模研究对这些系统的关键组件进行了系统评估，并得出以下关键发现：（1）基于激光雷达的协同配置表现出最高的感知性能；（2）车-路（V2I）和车-车（V2V）通信在不同融合方案下展现出不同的协同感知性能；（3）协同感知错误的增加可能导致更高频率的驾驶违规行为；（4）在线运行时，协同感知系统对通信干扰不具备鲁棒性。我们的研究结果揭示了协同感知系统关键组件中存在的潜在风险和脆弱性。我们希望我们的发现能更好地促进协同感知系统的设计与修复。