DocumentCode
3463598
Title
Nonlinear filter road vehicle model development
Author
Wada, Massaki ; Yoon, Kangsup ; Hashimoto, Hideki
Author_Institution
Inst. of Ind. Sci., Tokyo Univ., Japan
fYear
2001
fDate
2001
Firstpage
734
Lastpage
739
Abstract
This paper describes the first results of the investigation efforts performed in the development of the high-accuracy multisensor vehicle state estimation scheme. The use of UKF (Unscented Kalman Filter) in the state estimation scheme and vehicle model development framework is proposed. The first nonlinear vehicle model developed in this framework is also described. The model is able to cope with vehicle slip using multisensor data from inertial sensors, odometry, and the D-GPS. The simulation results indicated that the scheme is able to significantly reduce the errors in vehicle state estimates and is also able to perform real time internal sensors calibration
Keywords
road vehicles; sensor fusion; state estimation; Kalman Filter; UKF; multisensor data; multisensor vehicle state estimation; sensors calibration; state estimation; unscented Kalman filter; vehicle model development; vehicle state estimates; Control systems; Global Positioning System; Nonlinear filters; Road vehicles; Robustness; Sensor phenomena and characterization; Sensor systems; State estimation; Vehicle driving; Vehicle safety;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Transportation Systems, 2001. Proceedings. 2001 IEEE
Conference_Location
Oakland, CA
Print_ISBN
0-7803-7194-1
Type
conf
DOI
10.1109/ITSC.2001.948751
Filename
948751
Link To Document