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 -