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。尽管计算机视觉在物体识别领域取得了显著进展，当前感知方法在复杂真实交通环境中仍面临困难。物理遮挡和有限的传感器视场等问题对单个车辆系统而言依然存在。基于车联网（V2X）技术的协同感知（CP）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CP的基本架构和关键组件，但针对最新创新（特别是V2X通信技术背景下）的全面综述仍显不足。为弥补这一空白，本文全面概述了CP技术的演进历程，涵盖从早期探索到最新发展，包括V2X通信技术的进步。此外，本文还提出了一个现代通用框架，用以阐释基于V2X的CP工作流程，助力对CP系统组件的结构化理解。更进一步，本文根据所解决的关键问题对当前主流的基于V2X的CP方法进行了分类，并在该分类体系内开展了广泛的文献综述，评估了现有数据集及仿真平台。最后，结合感知与V2X通信技术的最新进展，探讨了自动驾驶中CP面临的开放挑战及未来发展方向。