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 also 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.

翻译：精确感知对于推动自动驾驶发展以及应对现代交通系统中的安全挑战至关重要。尽管计算机视觉在目标识别领域取得了显著进步，但当前的感知方法在复杂的真实交通环境中仍面临困难。对于单个车辆系统而言，物理遮挡和传感器视野受限等挑战依然存在。基于车联万物技术的协同感知已成为克服这些障碍并提升驾驶自动化系统的解决方案。虽然已有研究探讨了协同感知的基本架构和关键组件，但针对最新创新（尤其是在车联万物通信技术背景下）的全面综述仍显不足。为填补这一空白，本文全面概述了协同感知技术的演进历程，涵盖从早期探索到最新发展，包括车联万物通信技术的进步。此外，本文提出了一种当代通用框架，以阐释基于车联万物的协同感知工作流程，有助于结构化理解协同感知系统组件。进一步地，本文根据现有基于车联万物的协同感知方法所解决的关键问题对其进行了分类。在此分类体系下，本文开展了广泛的文献综述，评估了现有数据集与仿真器。最后，结合感知技术与车联万物通信的进展，讨论了自动驾驶中协同感知面临的开放性挑战及未来方向。