Vehicle and Road State Estimation Using Interacting Multiple Model Approach
This article describes the estimation algorithm of the vehicle state and the road condition, which is formulated on the basis of the interacting multiple model algorithm. Ten system modes of tire are modeled (considered nonlinearity) according to the road friction. Ten system modes are provided for switching from one mode to another in a probabilistic manner. The mode probabilities and states of vehicle are estimated based on extended Kalman filter (EKF). This algorithm is evaluated in the simulation study. Simulation results show that the algorithm is effective in the state estimation of vehicle and road surface.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9780415436168
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Supplemental Notes:
- Abstract reprinted with permission from Taylor and Francis.
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Corporate Authors:
Taylor & Francis
4 Park Square, Milton Park
Abingdon, United Kingdom OX14 4RN -
Authors:
- Tsunashima, H
- Murakami, M
- Miyata, J
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Conference:
- 19th Symposium of the International Association for Vehicle System Dynamics
- Location: Milan , Italy
- Date: 2005-8-28 to 2005-9-2
- Publication Date: 2006
Language
- English
Media Info
- Media Type: Print
- Features: Figures; References; Tables;
- Pagination: pp 750-758
- Monograph Title: Proceedings of the 19th Symposium of the International Association for Vehicle System Dynamics
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Serial:
- Vehicle System Dynamics
- Volume: 44
- Issue Number: Supplement
- Publisher: Taylor & Francis
- ISSN: 0042-3114
- EISSN: 1744-5159
- Serial URL: https://www.tandfonline.com/toc/nvsd20/current
Subject/Index Terms
- TRT Terms: Algorithms; Estimation theory; Friction; Highways; Kalman filtering; Rolling contact; Simulation; Vehicles
- Uncontrolled Terms: Models; Side slip angle
- Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01046777
- Record Type: Publication
- ISBN: 9780415436168
- Files: TRIS
- Created Date: Apr 18 2007 1:36PM