Intelligent vehicle can automatic drive

so the drive fatigue can be avoided totally and drive safety can be improved markedly. The research of intelligent vehicle is important aspect of intelligent transportation system. In order to ensure reliable navigating

the navigation mark should keep clean and clear. When the navigation mark becomes blur and smudges

the correct rate of the mark recognition descends

and the navigation reliability of intelligent vehicle also descends. In order to settle the problem

the method of recognizing blur and smudge navigation lane is studied by using fuzzy neural network for JLUIV-2 vision navigation intelligent vehicle. Two fuzzy neural network models are developed. One model is made up of 5 layers

its fuzzification function is a normal distribution probability function

another model has 6 layers

and its fuzzification function isπfunction. The modified quick BP algorithm is used to train the two fuzzy neural networks. Practical recognizing experiments are made by using image of blur and smudge stripe navigation mark. The results show the two fuzzy neural networks can effectively recognize the blur and smudge lane of JLUIV-2 intelligent vehicle. In order to satisfy the real-time requirement

a 10×300 interesting area abstracted form 222×300 image is processed in navigation.