基于车路协同的车辆定位算法研究

罗文慧; 董宝田; 王泽胜

doi:10.3969/j.issn.0258-2724.2018.05.026

基于车路协同的车辆定位算法研究

doi: 10.3969/j.issn.0258-2724.2018.05.026

详细信息

作者简介:
罗文慧（1983—），女，博士研究生，研究方向为智能运输系统理论与技术，E-mail： 14114230@bjtu.edu.cn

通讯作者:
董宝田（1956—），男，教授，博士生导师，研究方向为铁路信息化，E-mail： btdong@bjtu.edu.cn

中图分类号: U491.54
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出版历程
- 收稿日期: 2018-01-15
- 刊出日期: 2018-10-01

Algorithm Based on Cooperative Vehicle Infrastructure Systems

摘要

摘要: 为解决道路交叉口车辆由于定位信号缺失或者延迟引起的车辆定位偏差较大的问题，提出了基于车路协同的协同地图匹配算法（cooperative map-matching，CMM）. 首先利用扩展Kalman滤波（extended Kalman filter，EKF）融合GPS与车载航位推算系统（vehicular dead reckoning，DR）信息作为协同地图匹配的预先定位；然后基于短程通讯技术实现车辆信息的交换与共享，在电子地图的基础上，利用道路约束实现车辆进一步定位. 为了验证算法的有效性，搭建了模拟真实场景的仿真环境进行实验. 研究结果表明：采用EKF融合GPS/DR数据的交叉口车辆定位平均偏差为9.09 m，相比GPS 的14.31 m，定位偏差减小30.87%；采用CMM算法的交叉口车辆，当参与CMM车辆数为7时，平均位置偏差为4.5 m，参与CMM车辆数为10辆时，平均位置偏差为2.75 m，相比EKF定位偏差减小69.74%.
- 智能交通 /
- 协同地图匹配 /
- 车路协同 /
- 车辆定位系统 /
- 扩展Kalman滤波 /
- 协同定位
Abstract: In order to determine the problem of vehicle positioning deviation caused by a lack and delay of the positioning signal, a cooperative map-matching（CMM）algorithm, based on the cooperative vehicle infrastructure system, is proposed in this paper. First, the information obtained by GPS and that obtained by vehicular dead reckoning（DR）were fused to obtain the initial position of cooperative map-matching using an extended Kalman filter（EKF）. Then, vehicle information was exchanged and shared based on dedicated short-range communication （DSRC）. On the basis of an electronic map, the further positioning of vehicles was accomplished using road constraints. In order to verify the effectiveness of the proposed algorithm, an environment to simulate real scenes was set up to conduct the experiments. The experimental results demonstrate that the average positioning deviation of vehicles at intersections using EKF, which fuses data obtained from GPS and DR, is 9.90 m. The positioning deviation decreased by 30.87%, when compared with the average deviation of GPS, which is 14.31 m. The proposed CMM algorithm has an average position deviation of 4.5 m when the number of vehicles involved is 7, and 2.75 m when the number of vehicles involved is 10. The positioning deviation decreased by 69.74%.
- intelligent transportation /
- cooperative map-matching /
- vehicle infrastructure cooperation /
- vehicle locating system /
- extended Kalman filter /
- cooperative localization

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致谢: 本文的研究工作得到兰州交通大学优秀科研团队项目(201701)的资助.

图 1 CMM算法流程

Figure 1. Algorithm flowchart of CMM

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图 2 CMM算法示意图（右方车辆为目标车辆）

Figure 2. Schematic diagram of CMM（the right one is the target vehicle）

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图 3 网络地图

Figure 3. Road map

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图 4 定位轨迹

Figure 4. Positioning trajectories

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图 5 定位偏差

Figure 5. Positioning deviation

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图 6 参与CMM的车辆数与定位平均偏差关系

Figure 6. Relationship between the number of vehicles in CMM and the average positioning deviation

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图 7 定位轨迹

Figure 7. Positioning trajectories

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图 8 定位偏差

Figure 8. Positioning deviation

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表 1 观测点GPS与EKF偏差比较

Table 1. Comparison of deviation between GPS and EKF of observation points

定位方法	观测点
定位方法	${P_1}$	${P_2}$	${P_3}$	${P_4}$	${P_5}$	${P_6}$
GPS	17.92	11.23	12.55	15.50	14.31	14.40
EKF	8.81	6.62	7.52	12.83	8.61	10.20

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表 2 CMM与其它定位方法的位置偏差比较

Table 2. Comparison of position deviation between CMM and other positioning methods

GPS	EKF	参与CMM算法的车辆数/辆
GPS	EKF	2	3	4	5	6	7
14.31	9.09	9.01	8.52	7.16	6.44	5.72	4.50

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表 3 EKF定位偏差理论值与实测值

Table 3. Comparison of EKF positioning deviations theoretical and experimental

数值来源	${P_1}$	${P_2}$	${P_3}$	${P_4}$	${P_5}$	${P_6}$
理论估计值	8.85	6.65	6.52	12.70	8.65	10.28
实测值	8.81	6.62	6.91	12.83	8.61	10.20

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