测绘通报 ›› 2025, Vol. 0 ›› Issue (6): 180-184.doi: 10.13474/j.cnki.11-2246.2025.0631

• 技术交流 • 上一篇    

面向车路协同的地理信息时空逻辑设计

冯佳1,2, 邢翀1, 晏松1, 戴帅1,3   

  1. 1. 中国人民公安大学交通管理学院, 北京 100038;
    2. 河南警察学院智慧交通警务工程技术研究中心, 河南 郑州 450064;
    3. 公安部道路交通安全研究中心, 北京 100005
  • 收稿日期:2024-06-25 发布日期:2025-07-04
  • 通讯作者: 戴帅。E-mail:905981896@qq.com
  • 作者简介:冯佳(1985—),男,博士生,讲师,工程师,主要研究方向为交通管理与数据分析。E-mail:23431486@qq.com
  • 基金资助:
    国家重点研发计划(2023YFB4302705);中国人民公安大学基础科研(2022JKF434);河南警察学院改革与实践项目(JY2023019)

Spatio-temporal logic design of geographic information for vehicle infrastructure cooperative system

FENG Jia1,2, XING Chong1, YAN Song1, DAI Shuai1,3   

  1. 1. School of Traffic Management, People Public Security University of China, Beijing 100038, China;
    2. Engineering Technology Centre for Smart Traffic Police of Henan, Henan Police college, Zhengzhou 450064, China;
    3. Research Institute for Road Safety of MPS, Beijing 100005, China
  • Received:2024-06-25 Published:2025-07-04

摘要: 车路协同体系关键环节即实现车辆、道路与规则之间的逻辑交互关系。针对传统交通环境下车辆管控不精细、交通规则监督覆盖不全面情况,为实现一般交通环境下交通行为全过程监管,本文基于GNSS和RTK的数字交通要素构建方式,提出交通时空数字阵列(TSDA)概念,设计数字交通规则的时空实现。以分米级精度实现了空间交通地理网创建,在车速识别、逆行判定、信号规则和车型监管4个规则范围内进行了多场景试验,并在高精电子地图中进行违法行为轨迹标注。结果表明,场景1中识别出预设超速违法行为;场景2中识别出预设车速与未按信号指示行驶行为;场景3中识别出预设未按信号指示行驶行为及部分逆行情况;场景4中识别出预设车型闯禁行、未按信号指示行驶行为及逆行情况。所预设的违法行为均被准确捕捉。

关键词: 智能交通, 违法识别, 时空数据逻辑, 交通地理信息, 数字交管, 高精度地图

Abstract: The vital section in vehicle infrastructure cooperative system would be the logically interaction connection between the vehicle,infrastructure and the traffic rule.Considering the roughly distinguishing on vehicle trajectory and incomprehensive cover of traffic rule,to realize a meticulous supervise on the traffic behaviors in a normal traffic environment,a method to form digital traffic component based on GDSS and TRK has been designed under the conception which named of TSDA,also the traffic rule has been carried out in a spatio-temporal way.The spatial transportation geographic network is established on decimeter level,where the experiments about over speed,inverse travelling,traffic signal violation and trespass have been implemented under kinds of scenes,meanwhile the vehicle violation trajectory is labeled in high precision electronic map.Experiments result show that the preset over speed violation is identified in scene 1; the preset over speed violation and traffic signal violation is identified in scene 2; the preset partial inverse travelling and traffic signal violation is identified in scene 3; the preset inverse travelling,traffic signal violation and trespass is identified in scene 4.All the preset violation is caught.

Key words: intelligent transportation, violation identification, spatio-temporal data logic, traffic geographic information, digital traffic management, high-precise map

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