Road condition estimation for automotive anti-skid control system based on BP neural network

Author

Bian, Mingyuan ; Li, Keqiang ; Jin, Dafeng ; Lian, Xiaomin

Author_Institution

State Key Lab of Automotive Safety & Energy, Tsinghua Univ., Beijing, China

Volume

2

fYear

2005

Firstpage

1017

Abstract

It´s important to estimate the road condition to obtain better performances of the vehicular antiskid systems. A road condition identification system based on BP neural network was proposed in this paper to monitor the change of road surface situations in real time by using vehicle dynamics parameters, and an adjusting algorithm was put forward to amend the output of neural network. Offline simulation was done for several driving conditions by using the field test data, and most kinds of road surface were recognized correctly, which testified the validity of the system.

Keywords

automobiles; backpropagation; control engineering computing; neurocontrollers; road safety; roads; vehicle dynamics; adjusting algorithm; automotive antiskid control system; backpropagation neural network; driving condition; field test data; offline driving simulation; road condition estimation; road condition identification system; road surface recognition; vehicle dynamics parameter; vehicular antiskid system; Automotive engineering; Condition monitoring; Control systems; Friction; Intelligent sensors; Neural networks; Road safety; System testing; Vehicle dynamics; Wheels;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronics and Automation, 2005 IEEE International Conference

Print_ISBN

0-7803-9044-X

Type

conf

DOI

10.1109/ICMA.2005.1626691

Filename

1626691