Paper Info
Title
Improving Estimation of Vehicle's Trajectory Using the Latest Global Positioning System With Kalman Filtering
Abstract
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-detection method 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.
Year
DOI
Venue
2011
10.1109/TIM.2011.2147670
Instrumentation and Measurement, IEEE Transactions
Keywords
Field
DocType
Global Positioning System,Kalman filters,geographic information systems,GPS location,IMM system,Kalman filtering,geographic-information-system data,global positioning system,interacting-multiple-model,iterated geometrical error-detection method,vehicle trajectory estimation,Geographic information system (GIS),Kalman filter (KF),global positioning system (GPS),trajectory prediction
Geographic information system,Mean squared prediction error,Simulation,Control engineering,Real-time computing,Kalman filter,Acceleration,Global Positioning System,Intelligent transportation system,Iterated function,Trajectory,Mathematics
Journal
Volume
Issue
ISSN
60
12
0018-9456
Citations 
PageRank 
References 
15
0.78
13
Authors
2
Name
Order
Citations
PageRank
Barrios, C.1383.29
Yuichi Motai223024.68