Details
| Original language | English |
|---|---|
| Title of host publication | 2023 American Control Conference (ACC) |
| Place of Publication | San Diego, USA |
| Pages | 1622-1628 |
| Number of pages | 7 |
| ISBN (electronic) | 979-8-3503-2806-6 |
| Publication status | Published - 2023 |
Publication series
| Name | Proceedings of the American Control Conference |
|---|---|
| ISSN (Print) | 0743-1619 |
Abstract
Advanced driver assistance systems (ADAS) have led to a steady improvement in driving comfort and safety. Knowledge of vehicle dynamics and perception of the vehicle's environment are necessary for optimized ADAS and autonomous driving. A crucial parameter influencing vehicle dynamics is the maximum friction coefficient between the tire and the road. As this coefficient cannot be measured economically in serial production cars via existing vehicle sensors, model-based estimation algorithms are a field of interest.In this paper, maximum friction coefficient estimation is presented using an unscented Kalman filter (UKF) based on onboard vehicle sensors such as a six degrees of freedom inertial measurement unit, height level sensors, and tie rod force sensors. The goal is to analyze the potential of these sensors for maximum friction coefficient estimation. First, a variance-based sensitivity analysis is used to analyze the physical vehicle model of a Dacia Duster. Second, model-based maximum friction coefficient estimation is implemented for the test vehicle and the results using different sensor settings are compared for driving maneuvers carried out on a test track with different road surfaces. Finally, model-based maximum friction coefficient estimation using these onboard vehicle sensors shows improved results compared to the UKF from previous works.
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2023 American Control Conference (ACC). San Diego, USA, 2023. p. 1622-1628 (Proceedings of the American Control Conference).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Analysis of the Potential of Onboard Vehicle Sensors for Model-based Maximum Friction Coefficient Estimation
AU - Lampe, Nicolas
AU - Ziaukas, Zygimantas
AU - Westerkamp, Clemens
AU - Jacob, Hans-Georg
N1 - Funding Information: 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.
PY - 2023
Y1 - 2023
N2 - Advanced driver assistance systems (ADAS) have led to a steady improvement in driving comfort and safety. Knowledge of vehicle dynamics and perception of the vehicle's environment are necessary for optimized ADAS and autonomous driving. A crucial parameter influencing vehicle dynamics is the maximum friction coefficient between the tire and the road. As this coefficient cannot be measured economically in serial production cars via existing vehicle sensors, model-based estimation algorithms are a field of interest.In this paper, maximum friction coefficient estimation is presented using an unscented Kalman filter (UKF) based on onboard vehicle sensors such as a six degrees of freedom inertial measurement unit, height level sensors, and tie rod force sensors. The goal is to analyze the potential of these sensors for maximum friction coefficient estimation. First, a variance-based sensitivity analysis is used to analyze the physical vehicle model of a Dacia Duster. Second, model-based maximum friction coefficient estimation is implemented for the test vehicle and the results using different sensor settings are compared for driving maneuvers carried out on a test track with different road surfaces. Finally, model-based maximum friction coefficient estimation using these onboard vehicle sensors shows improved results compared to the UKF from previous works.
AB - Advanced driver assistance systems (ADAS) have led to a steady improvement in driving comfort and safety. Knowledge of vehicle dynamics and perception of the vehicle's environment are necessary for optimized ADAS and autonomous driving. A crucial parameter influencing vehicle dynamics is the maximum friction coefficient between the tire and the road. As this coefficient cannot be measured economically in serial production cars via existing vehicle sensors, model-based estimation algorithms are a field of interest.In this paper, maximum friction coefficient estimation is presented using an unscented Kalman filter (UKF) based on onboard vehicle sensors such as a six degrees of freedom inertial measurement unit, height level sensors, and tie rod force sensors. The goal is to analyze the potential of these sensors for maximum friction coefficient estimation. First, a variance-based sensitivity analysis is used to analyze the physical vehicle model of a Dacia Duster. Second, model-based maximum friction coefficient estimation is implemented for the test vehicle and the results using different sensor settings are compared for driving maneuvers carried out on a test track with different road surfaces. Finally, model-based maximum friction coefficient estimation using these onboard vehicle sensors shows improved results compared to the UKF from previous works.
UR - http://www.scopus.com/inward/record.url?scp=85167819590&partnerID=8YFLogxK
U2 - 10.23919/ACC55779.2023.10156574
DO - 10.23919/ACC55779.2023.10156574
M3 - Conference contribution
SN - 978-1-6654-6952-4
T3 - Proceedings of the American Control Conference
SP - 1622
EP - 1628
BT - 2023 American Control Conference (ACC)
CY - San Diego, USA
ER -