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智能网联车路云协同系统架构与关键技术研究综述

丁飞 张楠 李升波 边有钢 童恩 李克强

丁飞, 张楠, 李升波, 边有钢, 童恩, 李克强. 智能网联车路云协同系统架构与关键技术研究综述. 自动化学报, 2022, 48(10): 1−23 doi: 10.16383/j.aas.c211108
引用本文: 丁飞, 张楠, 李升波, 边有钢, 童恩, 李克强. 智能网联车路云协同系统架构与关键技术研究综述. 自动化学报, 2022, 48(10): 1−23 doi: 10.16383/j.aas.c211108
Ding Fei, Zhang Nan, Li Sheng-Bo, Bian You-Gang, Tong En, Li Ke-Qiang. A survey of architecture and key technologies of intelligent connected vehicle-road-cloud cooperation system. Acta Automatica Sinica, 2022, 48(10): 1−23 doi: 10.16383/j.aas.c211108
Citation: Ding Fei, Zhang Nan, Li Sheng-Bo, Bian You-Gang, Tong En, Li Ke-Qiang. A survey of architecture and key technologies of intelligent connected vehicle-road-cloud cooperation system. Acta Automatica Sinica, 2022, 48(10): 1−23 doi: 10.16383/j.aas.c211108

智能网联车路云协同系统架构与关键技术研究综述

doi: 10.16383/j.aas.c211108
基金项目: 国家自然科学基金(61871446, 61872423), 工业和信息化部产业技术基础公共服务平台项目(2019-00892-3-1), 工业和信息化部通信软科学研究项目(2019-R-26), 江苏省重点研发计划(BE2020084-1)和江苏省“六大人才高峰”高层次人才资助项目(DZXX-008)资助
详细信息
    作者简介:

    丁飞:南京邮电大学物联网学院副教授. 2010年获东南大学博士学位. 主要研究方向为智能网联汽车通信与网络技术, 边缘智能与协同计算技术. 本文通信作者. E-mail: dingfei@njupt.edu.cn

    张楠:南京邮电大学物联网学院硕士研究生. 主要研究方向为C-V2X技术和边缘智能技术. E-mail: zhangnan4899@163.com

    李升波:清华大学车辆与运载学院教授. 2009年获清华大学博士学位. 主要研究方向为自动驾驶与智能汽车, 强化学习与最优控制, 群体智能与分布式控制, 驾驶状态监控与驾驶辅助. E-mail: lishbo@tsinghua.edu.cn

    边有钢:湖南大学机械与运载工程学院副教授. 2019年获清华大学博士学位. 主要研究方向为协同控制, 智能控制及其在智能网联车辆的应用. E-mail: byg10@foxmail.com

    童恩:中国移动−南京邮电大学5G联合创新中心教授级高工. 主要研究方向为智能网联汽车通信与网络技术, 智能通信与信息系统. E-mail: tonge@js.chinamobile.com

    李克强:中国工程院院士, 清华大学车辆与运载学院教授, 国家智能网联汽车创新中心首席科学家. 1995年获重庆大学博士学位. 主要研究方向为智能网联汽车, 汽车系统动力, 汽车电子与智能控制. E-mail: likq@tsinghua.edu.cn

A Survey of Architecture and Key Technologies of Intelligent Connected Vehicle-road-cloud Cooperation System

Funds: Supported by National Natural Science Foundation of China (61871446, 61872423), Basic Public Service Platform Project of Industrial Technology of the Ministry of Industry and Information Technology (2019-00892-3-1), Communication Soft Science Research Project of the Ministry of Industry and Information Technology (2019-R-26), Key R & D Plan of Jiangsu Province (BE2020084-1), and “Six Talent Peaks” High Level Talent Funding Project of Jiangsu Province (DZXX-008)
More Information
    Author Bio:

    DING Fei Associate professor at the School of Internet of Things, Nanjing University of Posts and Telecommunications. He received his Ph.D. degree from Southeast University in 2010. His research interest covers communication and networking technology of intelligent connected vehicles, and edge intelligence and collaborative computing technology. Corresponding author of this paper

    ZHANG Nan Master student at the School of Internet of Things, Nanjing University of Posts and Telecommunications. Her research interest covers cellular vehicle-to-everything (C-V2X) technology and edge intelligence technology

    LI Sheng-Bo Professor at the School of Vehicle and Mobility, Tsinghua University. He received his Ph.D. degree from Tsinghua Univer-sity in 2009. His research interest covers autonomous driving and intelligent vehicles, reinforcement learning and optimal control, swarm intelligence and distributed control, and driver state monitoring and driving assistance

    BIAN You-Gang Associate professor at the College of Mechanical and Vehicle Engineering, Hunan University. He received his Ph.D. degree from Tsinghua University in 2019. His research interest covers cooperative control, intelligent control, and their applications to connected and automated vehicles

    TONG En Professor at 5G Joint Innovation Center of China Mobile & Nanjing University of Posts and Telecommunications. His research interest covers communication and networking technology of intelligent connected vehi-cles, and intelligent communication and information system

    LI Ke-Qiang Academician of Chinese Academy of Engineering, professor at the School of Vehicle and Mobility, Tsinghua University, chief scientist of National Innovation Center of Intelligent and Connected Vehicles. He received his Ph.D. degree from Chongqing University in 1995. His research interest covers intelligent and connected vehicles, vehicle system dynamics, vehicle electronics and intelligent control

  • 摘要: 随着汽车产业电动化、智能化、网联化、共享化的发展驱动, 全球主要强国均将智能网联汽车列为国家战略发展方向. 蜂窝车联网、边缘计算网络和高精度定位系统的技术发展, 为车车、车路、车人和车云系统的全面融合提供了有效支撑. 车辆、道路、云平台与蜂窝车联网(Cellular vehicle-to-everything, C-V2X)网络的融合, 加速打通车内与车外、路面与路侧、云上与云间的信息互通, 为实现车路云一体化的融合感知、群体决策及协同控制提供了重要基础. 首先, 梳理了智能网联车路云协同系统架构与关键技术, 对该领域的演进特征、发展制约因素进行了总体概述. 其次, 阐述了新型车路云协同系统、智能网联C-V2X通信系统、云控系统和车路云协同测试系统的架构设计与工作原理. 然后, 从C-V2X组网、融合定位、测试评价角度, 介绍了车路云协同系统融合V2X网络、融合定位的技术演进与研究进展, 给出了智能网联场景的仿真平台、实车测试及评价指标. 最后, 对智能网联车路云协同系统的协同组网与控制、互操作、边缘智能服务和安全技术层面的发展趋势进行了展望.
  • 图  1  智能网联车路云协同系统逻辑框架

    Fig.  1  Logical framework of the IC-VRCCS

    图  2  智能网联车辆V2X应用示意架构

    Fig.  2  Schematic architecture of V2X application of the ICV

    图  3  面向车路云一体化融合的云控系统架构[30]

    Fig.  3  Cloud control system architecture oriented to vehicle-road-cloud integration[30]

    图  4  云控应用平台示意架构

    Fig.  4  Schematic architecture of cloud control application platform

    图  5  智能网联汽车测试场景构建示意图

    Fig.  5  Schematic diagram of ICV test scenario construction

    图  6  C-V2X协议栈与传输控制

    Fig.  6  C-V2X protocol stack and transmission control

    图  7  MEC 与 C-V2X 融合测试床组网架构图

    Fig.  7  Networking architecture of MEC and C-V2X integration testbed

    图  8  协同组网与控制场景

    Fig.  8  NR-V2X typical application scenario demand index

    表  1  云控系统数据流向与数据类型示例

    Table  1  Example of data flow direction and interactive data types of cloud control system

    数据流向数据交互对象数据对象
    上行边缘云→区域云
    区域云→中心云
    车路融合感知信息
    动态交通事件信息
    交通状态信息
    动态交通管理信息
    下行区域云→边缘云
    中心云→区域云
    交通状态信息
    地图信息
    动态交通管理信息
    车辆运管信息
    下载: 导出CSV

    表  2  车联网技术与标准的对应关系

    Table  2  The relationship between car networking technology and standards

    标准DSRCC-ITSC-V2X
    LTE-V2XNR-V2X
    通信协议IEEE 802.11p
    IEEE1609.2/3/4
    SAE J2735
    SAE J2945
    IEEE 802.11p
    ETSI specifications
    (ITS-G5/C-ITS protocol stack)
    3GPP R14,15
    SAE J3161
    3GPP R16
    SAE J3161
    下载: 导出CSV

    表  3  MEC与C-V2X融合场景示例

    Table  3  Example of MEC and C-V2X integration scenarios

    交互场景应用交互场景应用
    单车与 MEC 交互V2X信息下发多车与 MEC 协同交互网联多车协同驾驶
    动态高精度地图
    车载信息增强 车路云协同感知
    车辆在线诊断
    单车与 MEC 及路侧智能设施交互危险驾驶提醒多车与 MEC 及路侧智能设施协同交互匝道合流辅助
    网联辅助驾驶 路口通行辅助
    网联安全接管 路网协同调度
    下载: 导出CSV

    表  4  现有典型融合定位方案的技术对比

    Table  4  Technical comparison of existing typical fusion positioning solutions

    定位技术方案传感器精度优势缺点
    GPS 与 IMU 融合定位[97]GPS & IMU7.2 m (RMSE)低成本低精度、信号可用性差
    带有道路标记检测与视觉融合的定位[98]GPS & IMU & 摄像头经度: 1.43 m
    纬度: 0.58 m
    低成本易受光照和观察角度的影响
    基于短距雷达的即时定位与地图构建[99]GPS & IMU & 雷达经度: 0.38 m
    纬度: 0.07 m
    低功耗、低成本、高精度对动态环境的鲁棒性低
    基于激光雷达的即时定位与地图构建[100]GPS & IMU & 激光雷达经度: 0.017 m
    纬度: 0.033 m
    高精度、对环境变化具有鲁棒性高成本、高功耗和处理能力需求、对天气状况敏感
    基于5G 的定位[101]5G通信设备水平方向: 0.3m ~ 10 m
    垂直方向: 2 m ~ 3 m
    高精度需要依托 5 G 基站
    V2V 和板载传感器定位[102]GPS & V2V通信 & 测距传感器0.6 m不依赖于所有车辆能够通信需要安装板载的测距传感器
    基于车距和信噪比的加权定位[103]GPS & V2V通信0.25 m ~ 0.85 m (结合
    网络大小配置)
    提高鲁棒性和准确性依赖车辆之间的通信链路
    基于 RTK 测量与惯性辅助的 GPS 定位[104]GPS & IMU & RTK 接收器0.05 m ~ 0.08 m提高定位的鲁棒性, 且可用于 GPS无覆盖区域的定位性需要部署RTK 基站功能
    下载: 导出CSV

    表  5  不同 GNSS 模式下的定位精度

    Table  5  Positioning accuracy in different GNSS modes

    GNSSGPS + GLO + GAL + BDSGPS + GLO + GALGPS + GALGPS + GLOGPS + BDSGPS
    水平位置精度位置速度时间1.5 m CEP1.5 m CEP1.5 m CEP1.5 m CEP1.5 m CEP1.5 m CEP
    星基增强系统1.0 m CEP1.0 m CEP1.0 m CEP1.0 m CEP1.0 m CEP1.0 m CEP
    RTK0.01 m +
    1 ppm CEP
    0.01m+
    1ppm CEP
    0.01m +
    1 ppm CEP
    0.01m +
    1 ppm CEP
    0.01m +
    1 ppm CEP
    0.01m +
    1 ppm CEP
    高程位置精度RTK0.01 m+
    1 ppm R50
    0.01 m +
    1 ppm R50
    0.01 m +
    1 ppm R50
    0.01 m +
    1 ppm R50
    0.01 m +
    1 ppm R50
    0.01 m +
    1 ppm R50
    下载: 导出CSV

    表  6  网联测试主要性能指标

    Table  6  Key performance indicators of connectivity test

    文献时延丢包率
    (可靠性)
    吞吐量通信范围数据包投递率信号强度
    [139140, 145]
    [146]
    [101]
    [147]
    [148]
    [144, 149]
    [150]
    下载: 导出CSV

    表  7  LTE-V2X典型场景通信性能要求[152]

    Table  7  LTE-V2X communication performance requirements in typical scenarios[152]

    典型场景有效范围 (m)绝对移动速度 (km/h)终端相对速度 (km/h)最大时延 (ms)单次传输可靠性 (%)累积传输可靠性 (%)
    主干道200501001009099
    限速高速公路3201602801008096
    不限速高速公路3202802801008096
    非视距/城市150501001009099
    城市交叉路口505010010095
    校园/商业区5030301009099
    碰撞前20801602095
    下载: 导出CSV

    表  8  NR-V2X典型应用场景需求指标[153]

    Table  8  Scenario diagram of cooperative networking and control[153]

    业务场景通信时延 (ms)数据速率 (Mbit/s)通信距离 (m)通信可靠性 (%)
    编队行驶10 ~ 250.012 ~ 6580 ~ 35099.99
    先进驾驶3 ~ 10010 ~ 53360 ~ 70099.999
    传感器扩展3 ~ 10010 ~ 100050 ~ 100099.999
    远程驾驶5上行: 25; 下行: 1无限制99.999
    下载: 导出CSV
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  • 收稿日期:  2021-11-23
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