Joint Wheel-Slip and Vehicle-Motion Estimation Based on Inertial, GPS, and Wheel-Speed Sensors
(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:
https://lup.lub.lu.se/record/7762238
- author
- Berntorp, Karl LU
- organization
- publishing date
- 2016
- 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
- 2016-04-04 13:22:17
- date last changed
- 2024-06-10 06:57:40
@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}}, keywords = {{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 = {{https://lup.lub.lu.se/search/files/6103397/7793265.pdf}}, doi = {{10.1109/TCST.2015.2470636}}, volume = {{24}}, year = {{2016}}, }