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Joint Wheel-Slip and Vehicle-Motion Estimation Based on Inertial, GPS, and Wheel-Speed Sensors

Berntorp, Karl LU (2016) In IEEE Transactions on Control Systems Technology 24(3). p.1020-1027
Abstract
Joint wheel-slip and vehicle-motion estimation is considered, based on measurements from wheel encoders, an inertial measurement unit, and a global positioning system (GPS). The proposed strategy effectively employs the Rao-Blackwellized particle-filtering framework using a kinematic model. Key vari- ables in active safety systems, such as longitudinal velocity, roll angle, and wheel slip for all four wheels are estimated. Results from a demanding field test shows the efficacy of the approach; the wheel slip and velocity can be estimated with an absolute accuracy of 0.018 and 0.25 m/s, respectively, measured as time- averaged root-mean-square errors, in periods of simultaneous aggressive braking and cornering. The corresponding differences... (More)
Joint wheel-slip and vehicle-motion estimation is considered, based on measurements from wheel encoders, an inertial measurement unit, and a global positioning system (GPS). The proposed strategy effectively employs the Rao-Blackwellized particle-filtering framework using a kinematic model. Key vari- ables in active safety systems, such as longitudinal velocity, roll angle, and wheel slip for all four wheels are estimated. Results from a demanding field test shows the efficacy of the approach; the wheel slip and velocity can be estimated with an absolute accuracy of 0.018 and 0.25 m/s, respectively, measured as time- averaged root-mean-square errors, in periods of simultaneous aggressive braking and cornering. The corresponding differences between best- and worst-case performance are 0.005 and 0.1 m/s. Results from a double lane-change maneuver indicate reliable velocity and slip estimation in periods of GPS outage. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
particle filtering, slip estimation, state estimation, Vehicle control
in
IEEE Transactions on Control Systems Technology
volume
24
issue
3
pages
1020 - 1027
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • Scopus:84941247677
  • WOS:000375273200021
ISSN
1063-6536
DOI
10.1109/TCST.2015.2470636
project
ENGROSS
language
English
LU publication?
yes
id
cacfd410-e496-4abb-82d7-ea7e9f295c30 (old id 7762238)
date added to LUP
2015-08-28 09:44:14
date last changed
2017-02-12 04:28:45
@article{cacfd410-e496-4abb-82d7-ea7e9f295c30,
  abstract     = {Joint wheel-slip and vehicle-motion estimation is considered, based on measurements from wheel encoders, an inertial measurement unit, and a global positioning system (GPS). The proposed strategy effectively employs the Rao-Blackwellized particle-filtering framework using a kinematic model. Key vari- ables in active safety systems, such as longitudinal velocity, roll angle, and wheel slip for all four wheels are estimated. Results from a demanding field test shows the efficacy of the approach; the wheel slip and velocity can be estimated with an absolute accuracy of 0.018 and 0.25 m/s, respectively, measured as time- averaged root-mean-square errors, in periods of simultaneous aggressive braking and cornering. The corresponding differences between best- and worst-case performance are 0.005 and 0.1 m/s. Results from a double lane-change maneuver indicate reliable velocity and slip estimation in periods of GPS outage.},
  author       = {Berntorp, Karl},
  issn         = {1063-6536},
  keyword      = {particle filtering,slip estimation,state estimation,Vehicle control},
  language     = {eng},
  number       = {3},
  pages        = {1020--1027},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Control Systems Technology},
  title        = {Joint Wheel-Slip and Vehicle-Motion Estimation Based on Inertial, GPS, and Wheel-Speed Sensors},
  url          = {http://dx.doi.org/10.1109/TCST.2015.2470636},
  volume       = {24},
  year         = {2016},
}