Details
| Original language | English |
|---|---|
| Title of host publication | IEEE Intelligent Vehicles Symposium (IV 2023) |
| ISBN (electronic) | 979-8-3503-4691-6 |
| Publication status | Published - 2023 |
Publication series
| Name | IEEE Intelligent Vehicles Symposium |
|---|---|
| ISSN (Print) | 1931-0587 |
| ISSN (electronic) | 2642-7214 |
Abstract
For optimizing advanced driver assistance systems (ADAS) and implementing autonomous driving, knowledge of vehicle dynamics and the perception of the vehicle's environment is required. A crucial parameter influencing vehicle dynamics is the maximum friction coefficient between tires and road. Since this coefficient cannot be measured practically without high technical effort, model-based estimation algorithms are used. However, estimating the maximum friction coefficient is only possible with sufficient vehicle dynamic excitation, as this coefficient is then observable. Since maneuvers with sufficient excitation are rare during normal driving, in this paper, different levels of active excitations are used to enable observability and estimation of the maximum friction coefficient during maneuvers with insufficient vehicle dynamic excitation. First, a vehicle dynamic model is presented and analyzed regarding the observability during active excitations. Second, model-based estimation using an unscented Kalman filter (UKF) is implemented for the test vehicle and the UKF parameters are tuned for active excitations. Finally, model-based maximum friction coefficient estimation using onboard vehicle sensors is enabled by using active excitations. The experimental results show that is possible to estimate the maximum friction coefficient with a low error as well as a low credibility for maneuvers with insufficient vehicle dynamic excitation by using active excitations.
Keywords
- Kalman filtering, active excitation, maximum friction coefficient, observability analysis, parameter estimation
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Automotive Engineering
- Mathematics(all)
- Modelling and Simulation
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IEEE Intelligent Vehicles Symposium (IV 2023). 2023. ( IEEE Intelligent Vehicles Symposium).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Active Excitations for Maximum Friction Coefficient Estimation
AU - Lampe, Nicolas
AU - Kortmann, Karl-Philipp
AU - Westerkamp, Clemens
AU - Jacob, Hans-Georg
N1 - Funding Information: ACKNOWLEDGMENT The authors would like to thank the Dr. Jürgen and Irmgard Ulderup foundation for funding this project and ZF Friedrichshafen AG for their support during the test drives. In addition, the authors would like to thank Mohamed Elerian for his contribution on the conduct of his master thesis.
PY - 2023
Y1 - 2023
N2 - For optimizing advanced driver assistance systems (ADAS) and implementing autonomous driving, knowledge of vehicle dynamics and the perception of the vehicle's environment is required. A crucial parameter influencing vehicle dynamics is the maximum friction coefficient between tires and road. Since this coefficient cannot be measured practically without high technical effort, model-based estimation algorithms are used. However, estimating the maximum friction coefficient is only possible with sufficient vehicle dynamic excitation, as this coefficient is then observable. Since maneuvers with sufficient excitation are rare during normal driving, in this paper, different levels of active excitations are used to enable observability and estimation of the maximum friction coefficient during maneuvers with insufficient vehicle dynamic excitation. First, a vehicle dynamic model is presented and analyzed regarding the observability during active excitations. Second, model-based estimation using an unscented Kalman filter (UKF) is implemented for the test vehicle and the UKF parameters are tuned for active excitations. Finally, model-based maximum friction coefficient estimation using onboard vehicle sensors is enabled by using active excitations. The experimental results show that is possible to estimate the maximum friction coefficient with a low error as well as a low credibility for maneuvers with insufficient vehicle dynamic excitation by using active excitations.
AB - For optimizing advanced driver assistance systems (ADAS) and implementing autonomous driving, knowledge of vehicle dynamics and the perception of the vehicle's environment is required. A crucial parameter influencing vehicle dynamics is the maximum friction coefficient between tires and road. Since this coefficient cannot be measured practically without high technical effort, model-based estimation algorithms are used. However, estimating the maximum friction coefficient is only possible with sufficient vehicle dynamic excitation, as this coefficient is then observable. Since maneuvers with sufficient excitation are rare during normal driving, in this paper, different levels of active excitations are used to enable observability and estimation of the maximum friction coefficient during maneuvers with insufficient vehicle dynamic excitation. First, a vehicle dynamic model is presented and analyzed regarding the observability during active excitations. Second, model-based estimation using an unscented Kalman filter (UKF) is implemented for the test vehicle and the UKF parameters are tuned for active excitations. Finally, model-based maximum friction coefficient estimation using onboard vehicle sensors is enabled by using active excitations. The experimental results show that is possible to estimate the maximum friction coefficient with a low error as well as a low credibility for maneuvers with insufficient vehicle dynamic excitation by using active excitations.
KW - Kalman filtering
KW - active excitation
KW - maximum friction coefficient
KW - observability analysis
KW - parameter estimation
UR - http://www.scopus.com/inward/record.url?scp=85167968511&partnerID=8YFLogxK
U2 - 10.1109/IV55152.2023.10186603
DO - 10.1109/IV55152.2023.10186603
M3 - Conference contribution
SN - 979-8-3503-4692-3
T3 - IEEE Intelligent Vehicles Symposium
BT - IEEE Intelligent Vehicles Symposium (IV 2023)
ER -