Details
| Original language | English |
|---|---|
| Pages (from-to) | 397-402 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 56 |
| Issue number | 3 |
| Early online date | 20 Dec 2023 |
| Publication status | Published - 2023 |
Abstract
Knowledge of the maximum friction coefficient µ max between tire and road is necessary for implementing autonomous driving. As this coefficient cannot be measured via existing serial vehicle sensors, µ max estimation is a challenging field in modern automotive research. In particular, model-based approaches are applied, which are limited in the estimation accuracy by the physical vehicle model. Therefore, this paper presents a data-based µ max estimation using serial vehicle sensors. For this purpose, recurrent artificial neural networks are trained, validated, and tested based on driving maneuvers carried out with a test vehicle showing improved results compared to the model-based algorithm from previous works.
Keywords
- intelligent autonomous vehicles, machine learning in estimation, maximum friction coefficient estimation, neural networks, parameter estimation, road condition
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
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In: IFAC-PapersOnLine, Vol. 56, No. 3, 2023, p. 397-402.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Neural Network based Tire-Road Friction Estimation Using Experimental Data
AU - Lampe, Nicolas
AU - Kortmann, Karl-Philipp
AU - Westerkamp, Clemens
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 - Knowledge of the maximum friction coefficient µ max between tire and road is necessary for implementing autonomous driving. As this coefficient cannot be measured via existing serial vehicle sensors, µ max estimation is a challenging field in modern automotive research. In particular, model-based approaches are applied, which are limited in the estimation accuracy by the physical vehicle model. Therefore, this paper presents a data-based µ max estimation using serial vehicle sensors. For this purpose, recurrent artificial neural networks are trained, validated, and tested based on driving maneuvers carried out with a test vehicle showing improved results compared to the model-based algorithm from previous works.
AB - Knowledge of the maximum friction coefficient µ max between tire and road is necessary for implementing autonomous driving. As this coefficient cannot be measured via existing serial vehicle sensors, µ max estimation is a challenging field in modern automotive research. In particular, model-based approaches are applied, which are limited in the estimation accuracy by the physical vehicle model. Therefore, this paper presents a data-based µ max estimation using serial vehicle sensors. For this purpose, recurrent artificial neural networks are trained, validated, and tested based on driving maneuvers carried out with a test vehicle showing improved results compared to the model-based algorithm from previous works.
KW - intelligent autonomous vehicles
KW - machine learning in estimation
KW - maximum friction coefficient estimation
KW - neural networks
KW - parameter estimation
KW - road condition
UR - http://www.scopus.com/inward/record.url?scp=85184353538&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2023.12.056
DO - 10.1016/j.ifacol.2023.12.056
M3 - Conference article
VL - 56
SP - 397
EP - 402
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 3
ER -