A Survey of Architecture and Key Technologies of Intelligent Connected Vehicle-road-cloud Cooperation System
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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.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.
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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 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 LTE-V2X NR-V2X 通信协议 IEEE 802.11p
IEEE 1609.2/3/4
SAE J2735
SAE J2945IEEE 802.11p
ETSI specifications
(ITS-G5/C-ITS protocol stack)3GPP R14,15
SAE J31613GPP R16
SAE J3161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 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 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 CEP0.01m +
1ppm CEP0.01m +
1 ppm CEP0.01m +
1 ppm CEP0.01m +
1 ppm CEP0.01m +
1 ppm CEP高程位置精度 RTK 0.01 m +
1 ppm R500.01 m +
1 ppm R500.01 m +
1 ppm R500.01 m +
1 ppm R500.01 m +
1 ppm R500.01 m +
1 ppm R50表 6 网联测试主要性能指标
Table 6 Key performance indicators of connectivity test
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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