面向智能网联汽车定位的协同地图匹配算法

陈伟; 杜路遥; 孔海洋; 傅率智; 郑洪江

面向智能网联汽车定位的协同地图匹配算法

1. 武汉理工大学自动化学院武汉 430070;
2. 上海博泰悦臻电子设备制造有限公司上海 200030;
3. 上海智能网联车载终端工程技术研究中心上海 200000

基金项目:

国家重点研发计划项目（2018YFB0105205）、湖北省技术创新专项重大项目（2019AAA025）、武汉理工大学双一流项目资助

详细信息

作者简介:
陈伟(1963-),博士,教授.研究方向:交通信息工程及控制.E-mail:greatchen@whut.edu.cn

通讯作者:
陈伟(1963-),博士,教授.研究方向:交通信息工程及控制.E-mail:greatchen@whut.edu.cn

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出版历程
- 网络出版日期: 2021-12-14

A Cooperative Map Matching Algorithm Applied in Intelligent and Connected Vehicle Positioning

1. School of Automation, Wuhan University of Technology, Wuhan 430070, China;
2. Shanghai PATEO Electronic Equipment Manufacturing Co., Ltd., Shanghai 200030, China;
3. Shanghai Engineering Technology Research Center for Intelligent and Connected Vehicle Terminals, Shanghai 200030, China

摘要

摘要: 为实现智能网联环境下低成本、高精度的车辆定位，研究了基于自适应遗传Rao-Blackwellized粒子滤波的协同地图匹配算法。利用联网车辆的定位信息和道路约束条件消除公共偏差，提高车辆定位精度。将自适应遗传算法引入到粒子滤波的重采样过程中，增加粒子的多样性，解决传统粒子滤波算法中容易出现的“粒子退化”和“粒子耗尽”问题。通过仿真实验与传统粒子滤波及卡尔曼平滑粒子滤波下的定位结果进行了对比，同时分析了不同联网车辆数目对定位精度的影响。通过实际测试验证了算法在实际应用中的定位效果。实测结果表明：以典型十字路口为例，在联网车辆数目为4的情况下，协同地图匹配算法的定位误差范围为1.67 m，分别为原始GNSS定位以及单车地图匹配定位结果的41.03%和56.80%。同时，该算法的统计定位精度（CEP）达到1.06 m，比GNSS原始定位精度提高了2.52 m，具有较好的定位效果。
- 智能交通 /
- 智能网联汽车 /
- 协同地图匹配 /
- 自适应遗传 Rao-Blackwellized粒子滤波 /
- 车载定位
Abstract: In order to achieve low-cost and high-precision vehicle positioning in the intelligent and connected environment,a cooperative map matching algorithm based on adaptive genetic Rao-Blackwellized particle filter is studied in this paper,improving the accuracy of vehicle positioning by using the real-time location data and road constraints of other connected vehicles. The adaptive genetic algorithm is introduced into the re-sampling process of the particle filter to increase the diversity of particles,so as to solve the problems of "particle degradation" and "particle exhaustion" that are prone to appear in traditional particle filter algorithms. Model of the algorithm is established and simulated. The positioning results under the traditional particle filter and Kalman smooth particle filter are compared,and the influence of the number of different connected vehicles on the positioning accuracy is analyzed. The experiment is completed in real-world and the performance of the algorithm is verified. The experimental results show that taking a typical intersection with four connected vehicles as an example,the range of position error of cooperative map matching is 1.67 m. It is only 41.03% and 56.80% of the traditional GNSS and the single map matching positioning results. At the same time,the circular error probable(CEP) of this algorithm is 1.06 m, which is 2.52 m higher than raw GNSS positioning result.
- intelligent transportation /
- intelligent and connected vehicle /
- cooperative map matching /
- adaptive genetic rao-blackwellized particle filter /
- vehicle positioning

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参考文献(19)

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