Improving Estimation of Vehicle's Trajectory Using the Latest Global Positioning System With Kalman Filtering
Improving Estimation of Vehicle's Trajectory Using the Latest Global Positioning System With Kalman Filtering
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DOI：
10.1109/tim.2011.2147670
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发表时间：
2011-12-01
影响因子：
5.6
通讯作者：
Motai, Yuichi
中科院分区：
文献类型：
--
作者：
Barrios, Cesar;Motai, Yuichi
本文提出了几种广泛的方法来预测汽车的未来位置。本文的目标是找到一种更准确的方法来预测汽车的未来位置提前3 s，使预测误差可以大大减少与全球定位系统（GPS）数据与地理信息系统（GIS）数据相结合的创新思想。该改进首先通过应用现有技术来外推当前GPS位置。综合卡尔曼滤波器（KF）的实现，以处理在不同的识别可能的状态，汽车可以被发现，这是确定为恒定的位置，恒定的速度，恒定的加速度，和恒定的颠簸不准确。然后，KF被设置为交互多模型（IMM）系统的一部分，该系统提供汽车的预测未来位置。为了减小IMM装置的预测误差，本文引入了一种基于GIS数据的迭代几何误差检测方法。假设汽车将保持在道路上;因此，对落在外面的未来位置的预测被相应地校正，从而大大减少了预测误差。实际的实验结果验证了我们提出的系统，减少预测误差的一半左右，它会是没有使用GIS数据。
This paper proposes several extensive methods to predict the future location of an automobile. The goals of this paper are to find a more accurate way to predict the future location of an automobile by 3 s ahead, so that the prediction error can be greatly reduced with the innovative idea of merging global-positioning-system (GPS) data with geographic-information-system (GIS) data. The improvement starts by applying existing techniques to extrapolate the current GPS location. Comprehensive Kalman filters (KFs) are implemented to deal with inaccuracy in the different identified possible states an automobile could be found in, which are identified as constant locations, constant velocity, constant acceleration, and constant jerks. Then, the KFs are set up to be part of a interacting-multiple-model (IMM) system that provides the predicted future location of the automobile. To reduce the prediction error of the IMM setup, this paper imports an iterated geometrical error-detectionmethod based on GIS data. The assumption that the automobile will remain on the road is made; therefore, the predictions of future locations that fall outside are corrected accordingly, making a great reduction to the prediction error. The actual experimental results validate our proposed system by reducing the prediction error to around half of what it would be without the use of GIS data.