Title :
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
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;
Conference_Titel :
Mechatronics and Automation, 2005 IEEE International Conference
Print_ISBN :
0-7803-9044-X
DOI :
10.1109/ICMA.2005.1626691
