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

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

丁飞^{1, 2,},
张楠^1,,
李升波^3,,
边有钢^4,,
童恩^2,,
李克强^3,

1.
南京邮电大学通信与网络技术国家工程研究中心南京 210003
2.
中国移动−南京邮电大学5G联合创新中心南京 210003
3.
清华大学汽车安全与节能国家重点实验室北京 100084
4.
湖南大学汽车车身先进设计制造国家重点实验室长沙 410082

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), 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

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出版历程
- 收稿日期: 2021-11-23
- 录用日期: 2022-04-27
- 网络出版日期: 2022-07-18
- 刊出日期: 2022-12-23

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

DING Fei^{1, 2
,},
ZHANG Nan^1
,,
LI Sheng-Bo^3
,,
BIAN You-Gang^4
,,
TONG En^2
,,
LI Ke-Qiang^3
,

1.
National Engineering Research Center for Communication and Network Technology, Nanjing University Posts and Telecommunications, Nanjing 210003
2.
5G Joint Innovation Center of China Mobile & Nanjing University of Posts and Telecommunications, Nanjing 210003
3.
State Key Laboratory of Automotive Safety & Energy, Tsinghua University, Beijing 100084
4.
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082

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 Research & Development 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, Na-njing 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

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.
- 车路云协同系统 /
- 蜂窝车联网 /
- 智能网联汽车 /
- 边缘计算网络 /
- 高精度定位
Abstract: With the development of electrification, intelligence, networking, and sharing in the automotive industry, all major countries have laid out the intelligent connected vehicle as a national strategy. The technological development of cellular vehicle-to-everything (C-V2X), edge computing networks, and high-precision positioning systems provide strong support for the comprehensive integration of the vehicle to vehicle, vehicle to the road, vehicle to pedestrian, and vehicle to cloud. Vehicles, roads, and cloud platforms are integrated with the C-V2X network, information exchange between the in-vehicle, road, and cloud with the out-vehicle, roadside, and different clouds will be accelerated, and the vehicle-road-cloud coordinated fusion perception, group decision, and collaborative control will also be established. Firstly, the architecture and key technologies of the intelligent connected vehicle-road-cloud cooperation system are combed, and its evolution characteristics and development constraints are summarized. Then, the architecture design and working principle of the new vehicle-road-cloud cooperation system, intelligent connected C-V2X communication system, the cloud control system, and vehicle-road-cloud cooperation test system are described. Besides, from the three aspects of C-V2X networking, fusion positioning, and test evaluation, the V2X network and fusion positioning-based technical evolution and research progress of vehicle-road-cloud cooperation system are introduced, and the simulation platform, real vehicle test, and evaluation index of intelligent networking scenario are also proposed. Finally, the development trends of the intelligent connected vehicle-road-cloud cooperation system in the aspects of collaborative networking and control, interoperability, edge intelligent service, and security technology are discussed.
- Vehicle-road-cloud cooperation system /
- cellular vehicle-to-everything /
- intelligent connected vehicle /
- edge computing network /
- high precision positioning

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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

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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

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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]

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图 4 云控应用平台示意架构

Fig. 4 Schematic architecture of cloud control application platform

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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

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图 6 C-V2X协议栈与传输控制

Fig. 6 C-V2X protocol stack and transmission control

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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

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图 8 协同组网与控制场景

Fig. 8 NR-V2X typical application scenario demand index

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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

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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	DSRC	C-ITS	C-V2X
标准	DSRC	C-ITS	LTE-V2X	NR-V2X
通信协议	IEEE 802.11p IEEE 1609.2/3/4 SAE J2735 SAE J2945	IEEE 802.11p ETSI specifications (ITS-G5/C-ITS protocol stack)	3GPP R14,15 SAE J3161	3GPP R16 SAE J3161

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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		网联多车协同驾驶
	车载信息增强		车路云协同感知
	车辆在线诊断		车路云协同感知
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

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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 & IMU	7.2 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: 0.3 m ~ 10 m 垂直方向: 2 m ~ 3 m	高精度	需要依托 5G 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

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表 5 不同 GNSS 模式下的定位精度

Table 5 Positioning accuracy in different GNSS modes

GNSS		GPS + GLO + GAL + BDS	GPS + GLO + GAL	GPS + GAL	GPS + GLO	GPS + BDS	GPS
水平位置精度	位置速度时间	1.5 m CEP	1.5 m CEP	1.5 m CEP	1.5 m CEP	1.5 m CEP	1.5 m CEP
	星基增强系统	1.0 m CEP	1.0 m CEP	1.0 m CEP	1.0 m CEP	1.0 m CEP	1.0 m CEP
	RTK	0.01 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	RTK	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	0.01 m + 1 ppm R50

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表 6 网联测试主要性能指标

Table 6 Key performance indicators of connectivity test

文献编号	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
[139−140, 145]	√	√	—	—	—	—
[146]	√	√	√	—	—	—
[101]	√	—	√	—	—	√
[147]	—	—	—	√	√	√
[148]	√	—	—	—	√	—
[144, 149]	√	√	—	√	—	—
[150]	√	√	—	—	√	—

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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	200	50	100	100	90	99
限速高速公路	320	160	280	100	80	96
不限速高速公路	320	280	280	100	80	96
非视距/城市	150	50	100	100	90	99
城市交叉路口	50	50	100	100	95	—
校园/商业区	50	30	30	100	90	99
碰撞前	20	80	160	20	95	—

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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 ~ 25	0.012 ~ 65	80 ~ 350	99.990
先进驾驶	3 ~ 100	10 ~ 53	360 ~ 700	99.999
传感器扩展	3 ~ 100	10 ~ 1000	50 ~ 1000	99.999
远程驾驶	5	上行 25; 下行 1	无限制	99.999

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