Accurate perception is essential for advancing autonomous driving and
addressing safety challenges in modern transportation systems. Despite
significant advancements in computer vision for object recognition, current
perception methods still face difficulties in complex real-world traffic
environments. Challenges such as physical occlusion and limited sensor field of
view persist for individual vehicle systems. Cooperative Perception (CP) with
Vehicle-to-Everything (V2X) technologies has emerged as a solution to overcome
these obstacles and enhance driving automation systems. While some research has
explored CP's fundamental architecture and critical components, there remains a
lack of comprehensive summaries of the latest innovations, particularly in the
context of V2X communication technologies. To address this gap, this paper
provides a comprehensive overview of the evolution of CP technologies, spanning
from early explorations to recent developments, including advancements in V2X
communication technologies. Additionally, a contemporary generic framework is
proposed to illustrate the V2X-based CP workflow, aiding in the structured
understanding of CP system components. Furthermore, this paper categorizes
prevailing V2X-based CP methodologies based on the critical issues they
address. An extensive literature review is conducted within this taxonomy,
evaluating existing datasets and simulators. Finally, open challenges and
future directions in CP for autonomous driving are discussed by considering
both perception and V2X communication advancements.

自动驾驶中准确的感知是推动该领域发展并解决安全挑战的关键。新兴的基于车联网技术的合作感知被提出作为一种解决方案，以克服当前感知方法在复杂真实交通环境中遇到的困难，并提升驾驶自动化系统的性能。本文通过全面综述合作感知技术的演进，并重点关注与车联网通信技术相关的最新研究，提出了一个当代通用框架来说明基于车联网的合作感知工作流程，分析了当前所解决的关键问题并进行了文献综述，最后探讨了自动驾驶中感知和车联网通信发展所面临的挑战和未来方向。