Details
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI) |
| ISBN (electronic) | 979-8-3503-8258-7 |
| Publication status | Published - 2023 |
| Event | Combined SDF and MFI Conference 2023 - Bonn, Germany Duration: 27 Nov 2023 → 29 Nov 2023 |
Publication series
| Name | International Conference on Multisensor Fusion and Information Integration for Intelligent Systems |
|---|---|
| ISSN (Print) | 2835-947X |
| ISSN (electronic) | 2767-9357 |
Abstract
An essential factor in ensuring driving stability and traffic safety of vehicles is knowledge about road condition and maximum tire-road friction potential. In previous research systems that either measure friction-related parameters or estimate the friction coefficient via a mathematical model have been proposed. However, the performance of these systems can be negatively impacted by environmental factors or require sufficient excitation in the form of tire slip, which is often too low under practical conditions. Therefore, this paper investigates if a more robust estimation can be achieved by fusing the information of multiple sources using a dynamic Bayesian network, which models the statistical relationship between different heterogeneous sources of information and the maximum friction coefficient. The algorithm is applied to data from a passenger vehicle to demonstrate the performance under real conditions. By using the dynamic Bayesian network the error can be reduced by up to 50%. To our knowledge this is the first time a dynamic Bayesian network is used to estimate road condition and friction coefficient.
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2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI). 2023. (International Conference on Multisensor Fusion and Information Integration for Intelligent Systems).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Friction and Road Condition Estimation using Dynamic Bayesian Networks
AU - Volkmann, Björn
AU - Kortmann, Karl-Philipp
N1 - Publisher Copyright: © 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - An essential factor in ensuring driving stability and traffic safety of vehicles is knowledge about road condition and maximum tire-road friction potential. In previous research systems that either measure friction-related parameters or estimate the friction coefficient via a mathematical model have been proposed. However, the performance of these systems can be negatively impacted by environmental factors or require sufficient excitation in the form of tire slip, which is often too low under practical conditions. Therefore, this paper investigates if a more robust estimation can be achieved by fusing the information of multiple sources using a dynamic Bayesian network, which models the statistical relationship between different heterogeneous sources of information and the maximum friction coefficient. The algorithm is applied to data from a passenger vehicle to demonstrate the performance under real conditions. By using the dynamic Bayesian network the error can be reduced by up to 50%. To our knowledge this is the first time a dynamic Bayesian network is used to estimate road condition and friction coefficient.
AB - An essential factor in ensuring driving stability and traffic safety of vehicles is knowledge about road condition and maximum tire-road friction potential. In previous research systems that either measure friction-related parameters or estimate the friction coefficient via a mathematical model have been proposed. However, the performance of these systems can be negatively impacted by environmental factors or require sufficient excitation in the form of tire slip, which is often too low under practical conditions. Therefore, this paper investigates if a more robust estimation can be achieved by fusing the information of multiple sources using a dynamic Bayesian network, which models the statistical relationship between different heterogeneous sources of information and the maximum friction coefficient. The algorithm is applied to data from a passenger vehicle to demonstrate the performance under real conditions. By using the dynamic Bayesian network the error can be reduced by up to 50%. To our knowledge this is the first time a dynamic Bayesian network is used to estimate road condition and friction coefficient.
UR - http://www.scopus.com/inward/record.url?scp=85182391823&partnerID=8YFLogxK
U2 - 10.1109/SDF-MFI59545.2023.10361516
DO - 10.1109/SDF-MFI59545.2023.10361516
M3 - Conference contribution
SN - 979-8-3503-8259-4
T3 - International Conference on Multisensor Fusion and Information Integration for Intelligent Systems
BT - 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI)
T2 - Combined SDF and MFI Conference 2023
Y2 - 27 November 2023 through 29 November 2023
ER -