NDLI: Platform and steady Kalman state observer design for Intelligent Vehicle based on visual guidance

Content Provider	IEEE Xplore Digital Library
Author	Zhang Rong-hui Wang Rong-ben You Feng Jia Hong-guang Chen Tao
Copyright Year	2008
Description	Author affiliation: Changchun Inst. of Opt., Fine Mech. & Phys., Chinese Acad. of Sci., Changchun (Zhang Rong-hui)
Abstract	State observer design is one of key technologies in research field of intelligent vehicle. Experiment platform, visual guidance intelligent vehicle JLUIV-5, is established by Jilin University Intelligent Vehicle Group firstly. The system structure and assistant navigation control system, and different image identify algorithms to recognize preview path and stops for variable illuminations are introduced. The dynamic response equation of steering control system was got by system identification experiment. By combined with the preview kinematics model, and two-degree steering dynamic model of vehicle, the steering Kalman filter mathematics model based on preview kinematics for intelligent vehicle was obtained. And observer is designed by applying steady Kalman filter theory. The simulation and experiment results, carry out in Jilin University Nanling Campus and Culture Center of Jilin Province, show that the image identify algorithms, and steady Kalman state observer designed by the proposed method has good adaptability for time-varying and parameters uncertain, it can satisfy intelligent vehicle trace the path reliably during outdoor experiment.
Starting Page	1
Ending Page	6
File Size	258589
Page Count	6
File Format	PDF
ISBN	9781424417056
DOI	10.1109/ICIT.2008.4608366
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicles Navigation Control systems Kalman filters Servomotors Mathematical model Observers CyberCar Navigation control technology Machine vision Steady Kalman filter theory Intelligent Vehicle
Content Type	Text
Resource Type	Article

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