Achieving fully autonomous driving with heightened safety and efficiency depends on vehicle-to-everything (V2X) cooperative perception (CP), which allows vehicles to share perception data, thereby enhancing situational awareness and overcoming the limitations of the sensing ability of individual vehicles. V2X CP is crucial for extending perception range, improving accuracy, and strengthening the decision-making and control capabilities of autonomous vehicles in complex environments. This paper provides a comprehensive survey of recent advances in V2X CP, introducing mathematical models of CP processes across various collaboration strategies. We examine essential techniques for reliable perception sharing, including agent selection, data alignment, and fusion methods. Key issues are analyzed, such as agent and model heterogeneity, perception uncertainty, and the impact of V2X communication constraints like delays and data loss on CP effectiveness. To inspire further advancements in V2X CP, we outline promising avenues, including privacy-preserving artificial intelligence (AI), collaborative AI, and integrated sensing frameworks, as pathways to enhance CP capabilities.

翻译：实现更高安全性和效率的完全自动驾驶依赖于车联网（V2X）协同感知（CP），该技术使车辆能够共享感知数据，从而增强环境态势感知能力，克服单车感知能力的局限。V2X协同感知对于扩展感知范围、提升感知精度、强化自动驾驶车辆在复杂环境中的决策与控制能力至关重要。本文全面综述了V2X协同感知的最新进展，系统介绍了不同协作策略下协同感知过程的数学模型。我们深入探讨了实现可靠感知共享的关键技术，包括智能体选择、数据对齐与融合方法。重点分析了智能体与模型异构性、感知不确定性、以及V2X通信约束（如延迟与数据丢失）对协同感知效能的影响等核心问题。为促进V2X协同感知的进一步发展，我们展望了隐私保护人工智能（AI）、协同人工智能与一体化感知框架等提升协同感知能力的前沿方向。