智能网联车辆编队控制技术作为智能交通领域的前沿方向，其研究对提升交通效率、保障交通安全及降低能源消耗具有重要意义。鉴于此，从文献计量分析、编队控制场景需求分析、编队控制体系及测试验证4个维度出发，对智能网联车辆编队控制的研究现状进行全面、深入的剖析与总结。结果表明，该领域目前处于快速发展期，研究主题从基础模型向“技术融合-系统协同-场景应用”演进；场景需求分析显示，物流运输等4类宏观场景与跟驰控制等4类微观场景在控制目标与技术瓶颈上差异显著；控制体系方面，分层协同架构、模块化功能设计和多模态控制方法为解决编队控制问题提供了新思路；测试验证体系需构建“仿真测试-实车测试-指标评估”闭环。既有编队控制研究在复杂场景泛化、大规模编队控制效率及通信鲁棒性等方面存在挑战，未来研究需聚焦多模态融合自适应控制策略、分层分布式架构及虚实融合的测试体系等方向，推动智能网联车辆编队向高鲁棒性、全场景适配发展，为交通运输领域的数字化与智能化转型提供坚实支撑。

As a cutting-edge direction in the field of intelligent transportation, the research on connected and automated vehicles（CAV）platoon control technology is of great significance for improving traffic efficiency, ensuring traffic safety, and reducing energy consumption. Given this, the article comprehensively and deeply analyzes and summarizes the research status of CAV platoon control from four dimensions: bibliometric analysis, platoon control scenario requirement analysis, platoon control system, and test and verification. The research results indicate that the field is currently in a period of rapid development, with research topics evolving from basic models to Technology Fusion-System Collaboration-Scenario Application. The scenario requirement analysis shows that there are significant differences in control objectives and technical bottlenecks between four macro scenarios, including logistics transportation, and four micro scenarios, such as car-following control. In terms of control system, hierarchical collaborative architecture, modular functional design, and multimodal control methods provide new ideas for solving platoon control problems. The testing and verification system needs to establish a closed loop of Simulation Testing-Real Vehicle Testing-Indicator Evaluation. There are challenges in the existing research on platoon control in terms of complex scene generalization, large-scale platoon control efficiency, and communication robustness. Future research needs to focus on multimodal fusion adaptive control strategies, hierarchical distributed architectures, and virtual-real fusion testing systems to promote the development of CAV platoons towards high robustness and full scene adaptation, providing solid support for the digital and intelligent transformation of the transportation industry.