智能网联交通系统的关键技术与发展

钱志鸿; 田春生; 郭银景; 王雪

doi:10.11999/JEIT190787

智能网联交通系统的关键技术与发展

doi: 10.11999/JEIT190787

1.
吉林大学通信工程学院长春 130012
2.
山东科技大学信息与电气工程学院青岛 266510

基金项目: 国家自然科学基金(61771219)，吉林大学基础科研项目(SXGJQY2017-9, 2017TD-19)，吉林大学研究生创新基金(101832018C022)

详细信息

作者简介:
钱志鸿：男，1957年生，教授，研究方向为无线网络通信技术，包括蓝牙、RFID, M2M, D2D、无线传感器网络及物联网等

田春生：男，1993年生，博士生，研究方向为D2D通信技术与物联网

郭银景：男，1966年生，教授，研究方向为网络通信、电磁兼容等

王雪：女，1984年生，副教授，研究方向为5G通信中的关键技术，具体包括D2D通信的模式选择、同步技术，以及物联网技术

通讯作者:
王雪　jluwangxue@163.com

中图分类号: TN92
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出版历程
- 收稿日期: 2019-10-16
- 修回日期: 2019-11-22
- 网络出版日期: 2019-11-30
- 刊出日期: 2020-01-21

The Key Technology and Development of Intelligent and Connected Transportation System

1.
College of Communication Engineering, Jilin University, Changchun 130012, China
2.
College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao, 266510, China

Funds: The National Natural Science Foundation of China (61771219), The Fundamental Research of Jilin University (SXGJQY2017-9, 2017TD-19), The Graduate Innovation Fund of Jilin University (101832018C022)

摘要

摘要: 该文梳理了国内外针对智能网联交通系统的相关研究，阐述了智能网联交通系统的架构和关键技术，分析了外部环境感知技术、车辆自主决策技术、控制执行技术以及车路协同技术等几个重点方向的研究进展。在分析总结已有文献的基础上，该文描述了未来智能网联交通系统的方案及其工作原理。未来智能网联交通系统应具备全程路径规划和精准定位功能，运用实时动态定位(RTK)技术和合成孔径雷达(SAR)技术，对运动或非运动物体(包括未装载GPS的物体)进行探测和定位，并保证在GPS信号弱或无信号(如隧道、室内)环境下和近距离、非可视情况下探测信号的连续性。系统还将运用移动边缘计算(MEC)理论，解决低时延、大规模网络接入等关键问题，运用大数据、云计算、物联网(IoTs)和移动通信技术，实现具有全局性、网络化的智能网联交通系统。
- 智能网联交通 /
- 车联网 /
- V2X /
- 实时动态定位 /
- 合成孔径雷达
Abstract: Some current works on intelligent and connected transportation system are presented, particularly focusing on the state of the art of the framework and key technologies in China or internationally, and the research development in some critical directions are elaborated including external environment perception, autonomous decision of vehicles, control execution and cooperative vehicle infrastructure system. On the basis of analyzing and summarizing the existing literature, the scheme of the future intelligent and connected transportation system and its working principle are described. The future intelligent and connected transportation system have the function of full path planning and precise, and the Real-Time Kinematic (RTK) and Synthetic Aperture Radar (SAR) technologies are used to detect and locate moving or non-moving objects, including those without GPS. And the continuity of the detection signal can be guaranteed in the environment where GPS signals are weak or non-signaled (e.g., tunnel, indoor) and the situation of close-range and non-visual. The Mobile Edge Computing (MEC) theory can also be used in the system to solve the key problems such as low latency and large-scale network access, and the big data, cloud computing, Internet of Things (IoTs) and mobile communication technologies are used to realize the global and networked intelligent and connected transportation system.
- Intelligent and connected transportation /
- Internet of Vehioles (IoVs) /
- Vehicle to Everything (V2X) /
- Real-Time Kinematic (RTK) /
- Synthetic Aperture Radar (SAR)

HTML全文

图 1 智能网联交通系统结构示意图

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图 2 直接式纵向结构控制

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图 3 分层式纵向结构控制

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图 4 V2X通信场景

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图 5 SD-V2X通信基本结构

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图 6 智能网联交通系统未来发展架构

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图 7 智能网联交通移动边缘计算体系结构

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表 1 3种不同感知技术对比

感知技术	优点	缺点	感知范围
视觉感知	实时性好，能耗较低，获取的信息量丰富	感知结果易受外界环境影响，3维物体识别精度较低	最远可实现250 m范围内物体的感知
激光感知	可精准识别3维物体距离信息，感知结果不易受外界环境影响	体积大，价格昂贵，无法完成无距离差异平面内物体感知	可完成300 m范围内直径1 cm物体的感知
微波感知	可精准识别3维物体距离信息，感知结果不易受外界环境影响	无法完成无距离差异平面内物体感知	取决于传感器的波长，一般可完成8～ 10 m内物体的感知

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表 2 不同控制执行技术的对比

控制执行技术			优点	缺点
横向控制	经典控制理论	PID	结构简单，可操作性好	线性模型，在多变量以及时变控制系统中具有局限性
	现代控制理论	最优控制	可使系统性能达到最优	对数学模型的依赖性较高
		滑模控制	非线性模型，系统鲁棒性好，响应速度较快	控制结果受外界不确定性影响较大
		自适应控制	对外部环境变化具有较强的鲁棒性	方法实时性相对较差
		模糊控制	无需借助精确的数学模型，对外部环境变化具有较强的鲁棒性	需借助研究人员的经验设置模糊规则
纵向控制	直接式结构控制		系统集成度高	过于依赖系统状态信息，模型非线性度较高
纵向控制	分层式结构控制		结构简单，易于实现，开发难度较低	忽略了参数不确定性以模型误差的影响，建模准确性相对较低

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