车路群智协同感知方法综述

洪东洋; 张慧; 张译匀; 王雨桐; 韩瑜珊; 曹原周汉; 李浥东

doi:10.11959/j.issn.2096-6652.202528

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车路群智协同感知方法综述

综述与展望 | 更新时间：2025-07-21

- 车路群智协同感知方法综述
- A review of collaborative intelligent perception methods for vehicle-road systems
- 智能科学与技术学报 2025年7卷第2期页码：143-164
- 作者机构：
  
  1.北京交通大学计算机科学与技术学院，北京100044
  2.中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京 100190
- 作者简介：
  
  [ "洪东洋（2005- ），女，北京交通大学计算机科学与技术学院硕士生，主要研究方向为计算机视觉、深度学习。" ]
  [ "张慧（1993- ），女，北京交通大学计算机科学与技术学院副教授，主要研究方向为复杂环境下的车辆感知、多传感器融合的协同检测、多模态目标检测、群体智能、平行视觉。" ]
  [ "张译匀（2002- ），女，北京交通大学计算机科学与技术学院本科生，主要研究方向为计算机视觉、深度学习。" ]
  [ "王雨桐（1994- ），女，博士，中国科学院自动化研究所多模态人工智能系统全国重点实验室副研究员，主要研究方向为计算机视觉、图像异常检测。" ]
  [ "韩瑜珊（1996- ），女，北京交通大学计算机科学与技术学院博士生，主要研究方向为计算机视觉。" ]
  [ "曹原周汉（1988- ），男，北京交通大学计算机科学与技术学院副教授，主要研究方向为计算机视觉、深度学习、自动驾驶。" ]
  [ "李浥东（1982- ），男，北京交通大学计算机科学与技术学院副院长（主持工作）、教授、博士生导师，交通大数据与人工智能教育部重点实验室主任，主要研究方向为大数据智能、数据隐私保护、先进计算、智能交通等。" ]
- 基金信息：
  
  中央高校基本科研业务费项目(2024JBMC045);国家自然科学基金青年基金项目(62203040)
- DOI：10.11959/j.issn.2096-6652.202528
  中图分类号： TP39
- 收稿日期：2025-03-07，
  
  修回日期：2025-06-09，
  
  纸质出版日期：2025-06-15
- 稿件说明：
移动端阅览
洪东洋,张慧,张译匀等.车路群智协同感知方法综述[J].智能科学与技术学报,2025,07(02):143-164.

HONG Dongyang,ZHANG Hui,ZHANG Yiyun,et al.A review of collaborative intelligent perception methods for vehicle-road systems[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):143-164.
洪东洋,张慧,张译匀等.车路群智协同感知方法综述[J].智能科学与技术学报,2025,07(02):143-164. DOI： 10.11959/j.issn.2096-6652.202528.

HONG Dongyang,ZHANG Hui,ZHANG Yiyun,et al.A review of collaborative intelligent perception methods for vehicle-road systems[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):143-164. DOI： 10.11959/j.issn.2096-6652.202528.

摘要

单车智能系统在感知层面存在一定的局限性，如超视距感知能力不足、缺乏全局环境感知视角、在极端场景下的感知稳定性受限等。为提升车辆感知能力，车路群智协同感知技术应运而生。该技术通过多个智能体（如车辆、基础设施等）共享感知信息，并对信息进行整合和分析，获取更全面、准确的环境认知。聚焦于车路群智协同感知方法的最新进展：首先，介绍车路群智协同感知方法的基础技术支撑；其次，根据数据处理和交互方法，将其划分为4类协同模式；然后，深入探讨了3种主要的协同感知方法以及常用的大规模协同感知数据集；最后，讨论了车路群智协同感知技术面临的挑战和未来研究展望。车路群智协同感知方法有助于弥补单一智能体感知的不足，实现对环境的全面感知。

Abstract

The intelligent system of a single vehicle has certain perception limitations

such as the inability to perceive beyond visual range

the lack of global perception capabilities

and restricted perception capabilities in extreme situations. To enhance vehicle perception capabilities

vehicle-road collaborative intelligent perception technology has emerged. This technology enables multiple intelligent agents (such as vehicles

infrastructure

etc.) to share perceived information

integrate and analyze it

and thereby gain a more comprehensive and accurate understanding of the environment. This paper focuses on the latest developments in vehicle-road collaborative intelligent perception methods. Firstly

it introduces the fundamental technological support for vehicle-road collaborative intelligent perception methods. Secondly

it categorizes collaborative modes into four types based on data processing and interaction methods. Then

it delves into three primary collaborative perception methods and commonly used large-scale collaborative perception datasets. Finally

it discusses the challenges faced by vehicle-road collaborative intelligent perception technology and the outlook for future research. Vehicle-road collaborative intelligent perception methods help to compensate for the deficiencies of single-agent perception and achieve comprehensive environmental perception.

关键词

Keywords

references

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