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首页> 外文期刊>Journal of Computers >A Vehicle Map-matching Algorithm based on Measure Fuzzy Sorting
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A Vehicle Map-matching Algorithm based on Measure Fuzzy Sorting

机译：一种基于测量模糊分类的车辆映射算法

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摘要

—The vehicle position obtained from GPS and dead reckoning is wildly applied to car navigation systems. However, the estimated position has an undesirable error due to the unknown GPS noise. To solve this problem, previous papers presented a method called "map-matching” to correct the position error. In this paper, we proposes a fuzzy ranking map matching algorithm based on measure factor. Comparing with other four algorithms, our algorithm improves in strategies of the error region determination, the road grid index and auto-adapted fuzzy sorting. To be specific, the error rectangle is firstly replaced by the error ellipse to reduce geometrical operation. Secondly, the grid index is adopted to accelerate the speed of filtering candidate road. At last, the relativity function and fuzzy sorting method help to sort the membership degree and to decide the matching road section. For the experiments, we implement a vehicle navigation system of five kinds of vehicle running status to testify the robustness and efficiency of this algorithm. The result shows that 96.7% of the GPS points are matched. In comparison with other algorithms, this algorithm had highest accuracy, which is of importance for vehicle navigation.
机译：- 从GPS获得的车辆位置和死亡估算是野外应用到汽车导航系统的。然而，由于未知的GPS噪声，估计位置具有不良误差。为了解决这个问题，之前的论文提出了一种称为“Map-匹配”的方法来纠正位置误差。在本文中，我们提出了一种基于测量因子的模糊排名地图匹配算法。与其他四种算法相比，我们的算法改善了策略错误区域确定，道路网格索引和自适应模糊分类。要具体，误差矩形首先由错误椭圆替换为降低几何操作。其次，采用网格指数加速过滤候选的速度道路。最后，相对函数和模糊分类方法有助于对成员程度进行排序和决定匹配的道路部分。对于实验，我们实施了五种车辆运行状态的车辆导航系统，以证明鲁棒性和效率该算法。结果表明，96.7％的GPS点匹配。与其他算法相比，该算法具有最高的精度，即车辆导航的重要性。

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