面向智能网联汽车定位的协同地图匹配算法

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。 

A Cooperative Map Matching Algorithm for Intelligent and Connected Vehicle Positioning

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.