Details
| Original language | English |
|---|---|
| Title of host publication | 22nd IFAC World Congress |
| Editors | Hideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita |
| Pages | 854-861 |
| Number of pages | 8 |
| Volume | 56 |
| Edition | 2 |
| ISBN (electronic) | 9781713872344 |
| Publication status | Published - 2023 |
Publication series
| Name | IFAC-PapersOnLine |
|---|---|
| Number | 2 |
| Volume | 56 |
| ISSN (electronic) | 2405-8963 |
Abstract
Keywords
- Estimation and Filtering, Sensing, Statistical Inference, Bayesian Methods, Sensor Integration and Perception, Sensor Integration, Perception
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
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22nd IFAC World Congress. ed. / Hideaki Ishii; Yoshio Ebihara; Jun-ichi Imura; Masaki Yamakita. Vol. 56 2. ed. 2023. p. 854-861 (IFAC-PapersOnLine; Vol. 56, No. 2).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Friction and Road Condition Estimation using Bayesian Networks
AU - Volkmann, Björn
AU - Kortmann, Karl-Philipp
AU - Seel, Thomas
N1 - Funding Information: The research for this paper was funded by the German Federal Ministry for Digital and Transport (BMDV) under the grant number 19F2132F within the mFund initiative. We thank Adil Murtaza Zuberi and Hauke Baumgärtel from Hella for their cooperation in quantifying the confusion matrix for the road condition sensor. Additionally, we thank Mirko Erich Schaper for training the SqueezeNet and preparing the required data set.
PY - 2023
Y1 - 2023
N2 - Knowledge about the maximum tire-road friction potential is an important factor to ensure the driving stability and traffic safety of the vehicle. Many authors proposed systems that either measure friction related parameters or estimate the friction coefficient directly via a mathematical model. However these systems can be negatively impacted by environmental factors or require a sufficient level of excitation in the form of tire slip, which is often too low under practical conditions. Therefore, this work investigates, if a more robust estimation can be achieved by fusing the information of multiple systems using a Bayesian network, which models the statistical relationship between the sensors and the maximum friction coefficient. First, the Bayesian network is evaluated over its entire domain to compare the inference process to all possible road conditions. After that, the algorithm is applied to data from a test vehicle to demonstrate the performance under real conditions.
AB - Knowledge about the maximum tire-road friction potential is an important factor to ensure the driving stability and traffic safety of the vehicle. Many authors proposed systems that either measure friction related parameters or estimate the friction coefficient directly via a mathematical model. However these systems can be negatively impacted by environmental factors or require a sufficient level of excitation in the form of tire slip, which is often too low under practical conditions. Therefore, this work investigates, if a more robust estimation can be achieved by fusing the information of multiple systems using a Bayesian network, which models the statistical relationship between the sensors and the maximum friction coefficient. First, the Bayesian network is evaluated over its entire domain to compare the inference process to all possible road conditions. After that, the algorithm is applied to data from a test vehicle to demonstrate the performance under real conditions.
KW - Estimation and Filtering
KW - Sensing
KW - Statistical Inference
KW - Bayesian Methods
KW - Sensor Integration and Perception
KW - Sensor Integration
KW - Perception
UR - http://www.scopus.com/inward/record.url?scp=85182395779&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2023.10.1672
DO - 10.1016/j.ifacol.2023.10.1672
M3 - Conference contribution
VL - 56
T3 - IFAC-PapersOnLine
SP - 854
EP - 861
BT - 22nd IFAC World Congress
A2 - Ishii, Hideaki
A2 - Ebihara, Yoshio
A2 - Imura, Jun-ichi
A2 - Yamakita, Masaki
ER -