A Cooperative Map Matching Algorithm for Intelligent and Connected Vehicle Positioning
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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以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。
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关键词:
- 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: A cooperative map-matching algorithm based on adaptive genetic Rao-Blackwellized particle filter is studied for low-cost and high-precision vehicle positioning in the intelligent and connected vehicle environment.The accuracy of vehicle positioning is improved 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, increasing the diversity of particles to solve the problems of"particle degradation"and"particle exhaustion"in traditional particle filters algorithms. The model of the algorithm is established and simulated. The positioning results under the traditional particle filter and Kalman particle filter are compared, with the influences of different connected vehicle numbers on the positioning accuracy analyzed. The experiment is completed in the real scene, and the performance of the algorithm is verified. The results show that taking a typical intersection with four connected vehicles as a case study, the range of position error of cooperative map matching is 1.67 m. It is only 41.03% and56.80% of the traditional GNSS and the single map matching positioning results, respectively. The circular error probable(CEP)of this algorithm is 1.06 m, which is 2.52 m higher than the raw GNSS positioning result. -
图 7 水平定位误差与联网车辆数目之间的关系
Figure 7. Relationship between horizontal position errors and the number of connected vehicles
图 8 公共偏差与联网车辆数目之间的关系
Figure 8. Relationship between the errors of common deviation and the number of connected vehicles
图 9 协方差行列式与联网车辆数目之间的关系
Figure 9. Relationship between covariance determinant and the number of connected vehicles
图 11 水平方向定位误差和协方差行列式
Figure 11. Horizontal positioning error and covariance determinant
图 14 水平方向定位误差累积分布曲线
Figure 14. Cumulative distribution curves of horizontal positioning errors
表 1 仿真参数设置
Table 1. Values of simulation parameters
参数 数值 参数 数值 单位 pc1 0.8 Δn 1 s pc2 0.6 σax 1 m/s2 pm1 0.1 σay 1 m/s2 pm2 0.001 σb 1 m/s2 β 1 σc 0.1 m/s2 Ns 6 σd 1 m/s2 Nv 4 σz 1 m n 200 
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表 2 50次蒙特卡洛实验定位结果均值
Table 2. Means of positioning result of 50 Monte Carlo experiments
项目 参数 粒子数目 50 100 150 200 静态粒子滤波 水平误差/m 3.67 3.49 3.35 3.22 协方差/m2 4.64 4.41 4.53 4.29 平均耗时/s 46.39 74.86 121.77 136.21 卡尔曼平滑粒子滤波 水平误差/m 2.44 2.19 2.04 1.93 协方差/m2 2.28 2.16 2.33 2.24 平均耗时/s 56.46 77.39 129.68 154.37 自适应遗传RBPF 水平误差/m 1.46 1.26 1.18 1.08 协方差/m2 1.59 1.57 1.62 1.56 平均耗时/s 60.36 94.17 148.59 184.11
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表 3 不同联网车辆数目下定位结果
Table 3. Positioning results under different numbers of connected vehicles
定位误差 GNSS 单车地图匹配 联网车辆数目/辆 2 3 4 5 6 7 8 9 10 水平误差/m 4.09 2.64 1.79 1.45 1.36 1.17 1.09 1.05 1.03 0.99 0.96 估计误差/m / 2.07 1.46 1.18 1.06 0.96 0.86 0.73 0.67 0.64 0.62 协方差/m2 6.62 2.53 2.17 1.91 1.62 1.53 1.50 1.46 1.44 1.39 1.38
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表 4 测试环境和参数设置
Table 4. Test environment and values of parameters
参数 设置 测试地点 湖北省武汉市某高校校园内 场景描述 天气晴朗的开阔室外 基准点/m (12 726 565.898 3 547 945.341) 通信方式 DSRC 通信频率/Hz 5.9 通信范围/m ≤1 500 车辆行驶速度/(km/h) 10~30 天线增益/dB 50 数据更新频率/Hz 10 数据输出频率/Hz 1
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表 5 GNSS，单车地图匹配以及协同地图匹配测试结果
Table 5. Experimental results of GNSS, single map matching, and cooperative map matching
东向误差/m 北向误差/m 水平误差/m 协方差行列式/m2 公共偏差估计误差/m CEP/m 4辆车地图匹配 1.14 1.19 1.67 1.83 1.36 1.06 单车地图匹配 2.09 2.16 2.94 2.74 2.25 2.13 原始GNSS定位 2.86 2.78 4.07 3.48 3.58
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