Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) as an essential component to achieve V2X can overcome the inherent limitations of individual perception, including occlusion and long-range perception. In this survey, we provide a comprehensive review of CP methods for V2X scenarios, bringing a profound and in-depth understanding to the community. Specifically, we first introduce the architecture and workflow of typical V2X systems, which affords a broader perspective to understand the entire V2X system and the role of CP within it. Then, we thoroughly summarize and analyze existing V2X perception datasets and CP methods. Particularly, we introduce numerous CP methods from various crucial perspectives, including collaboration stages, roadside sensors placement, latency compensation, performance-bandwidth trade-off, attack/defense, pose alignment, etc. Moreover, we conduct extensive experimental analyses to compare and examine current CP methods, revealing some essential and unexplored insights. Specifically, we analyze the performance changes of different methods under different bandwidths, providing a deep insight into the performance-bandwidth trade-off issue. Also, we examine methods under different LiDAR ranges. To study the model robustness, we further investigate the effects of various simulated real-world noises on the performance of different CP methods, covering communication latency, lossy communication, localization errors, and mixed noises. In addition, we look into the sim-to-real generalization ability of existing CP methods. At last, we thoroughly discuss issues and challenges, highlighting promising directions for future efforts. Our codes for experimental analysis will be public at https://github.com/memberRE/Collaborative-Perception.

翻译：车联万物（V2X）自动驾驶为发展新一代智能交通系统开辟了富有前景的方向。作为实现V2X的关键组成部分，协同感知（CP）能够克服单车辆感知的固有限制，包括遮挡和远程感知。本综述全面回顾了面向V2X场景的CP方法，为学术界提供深刻而细致的理解。具体而言，我们首先介绍了典型V2X系统的架构和工作流程，提供了更广阔的视角来理解整个V2X系统及CP在其中扮演的角色。随后，我们系统总结并分析了现有V2X感知数据集与CP方法。特别地，我们从多个关键视角对大量CP方法进行阐述，包括协同阶段、路侧传感器部署、延迟补偿、性能-带宽权衡、攻防、位姿对齐等。此外，我们开展了广泛的实验分析来比较和检验当前CP方法，揭示了一些必要且尚未探索的见解。具体而言，我们分析了不同方法在不同带宽下的性能变化，深入剖析了性能-带宽权衡问题。同时，我们检验了不同激光雷达探测范围下的方法表现。为研究模型鲁棒性，我们进一步探究了多种模拟真实世界噪声对不同CP方法性能的影响，涵盖通信延迟、有损通信、定位误差及混合噪声。除此之外，我们考察了现有CP方法的仿真到真实泛化能力。最后，我们深入讨论了相关问题与挑战，并指明了未来研究的重要方向。实验分析代码将开源至https://github.com/memberRE/Collaborative-Perception。