Details
| Original language | English |
|---|---|
| Title of host publication | 2006 IEEE International Conference on Mechatronics, ICM |
| Place of Publication | Budapest |
| Pages | 137-143 |
| Number of pages | 7 |
| Publication status | Published - 2006 |
| Event | 2006 IEEE International Conference on Mechatronics, ICM - Budapest, Hungary Duration: 3 Jul 2006 → 5 Jul 2006 |
Abstract
In this paper the road surface condition is detected based on multi-sensor data fusion for an improved preconditioning of automotive control systems. It involves the use of different measuring systems in three levels: the environment description, the slip based statistical slippery recognition and the reactive friction detection and adaptation. The signals of optical and acoustic sensors build the inputs of a pre-processing block, where a specific frequency- and statistical analysis is implemented. To estimate the road state a decision block based on a fuzzy expert system has been defined and tested. A further topic of this contribution is the use of a mobile friction measuring platform for investigations of the texture road impact on the grip. The friction coefficient between road and a small rubber wheel is measured at a high driving slip rate. Simultaneously the road profile is captured with a laser and roughness descriptors are computed. The regression between descriptors and grip is obtained by an artificial neural network, which can be used for prognostication after learning.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Mechanical Engineering
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2006 IEEE International Conference on Mechatronics, ICM. Budapest, 2006. p. 137-143 4018346.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Road-wheel interaction in Vehicles
T2 - 2006 IEEE International Conference on Mechatronics, ICM
AU - Heimann, Bodo
AU - Bouzid, Noamen
AU - Trabelsi, Ahmed
PY - 2006
Y1 - 2006
N2 - In this paper the road surface condition is detected based on multi-sensor data fusion for an improved preconditioning of automotive control systems. It involves the use of different measuring systems in three levels: the environment description, the slip based statistical slippery recognition and the reactive friction detection and adaptation. The signals of optical and acoustic sensors build the inputs of a pre-processing block, where a specific frequency- and statistical analysis is implemented. To estimate the road state a decision block based on a fuzzy expert system has been defined and tested. A further topic of this contribution is the use of a mobile friction measuring platform for investigations of the texture road impact on the grip. The friction coefficient between road and a small rubber wheel is measured at a high driving slip rate. Simultaneously the road profile is captured with a laser and roughness descriptors are computed. The regression between descriptors and grip is obtained by an artificial neural network, which can be used for prognostication after learning.
AB - In this paper the road surface condition is detected based on multi-sensor data fusion for an improved preconditioning of automotive control systems. It involves the use of different measuring systems in three levels: the environment description, the slip based statistical slippery recognition and the reactive friction detection and adaptation. The signals of optical and acoustic sensors build the inputs of a pre-processing block, where a specific frequency- and statistical analysis is implemented. To estimate the road state a decision block based on a fuzzy expert system has been defined and tested. A further topic of this contribution is the use of a mobile friction measuring platform for investigations of the texture road impact on the grip. The friction coefficient between road and a small rubber wheel is measured at a high driving slip rate. Simultaneously the road profile is captured with a laser and roughness descriptors are computed. The regression between descriptors and grip is obtained by an artificial neural network, which can be used for prognostication after learning.
UR - http://www.scopus.com/inward/record.url?scp=34250813835&partnerID=8YFLogxK
U2 - 10.1109/ICMECH.2006.252511
DO - 10.1109/ICMECH.2006.252511
M3 - Conference contribution
AN - SCOPUS:34250813835
SN - 0780397134
SN - 9780780397132
SP - 137
EP - 143
BT - 2006 IEEE International Conference on Mechatronics, ICM
CY - Budapest
Y2 - 3 July 2006 through 5 July 2006
ER -