Lane Detection Based on Classification of Lane Geometrical Model

As is known to all, there are thousands of people died in traffic accidents in the world every year and 80% of the traffic accidents are attention. In order to reduce the occurrence of car accidents, the design of Cognitive Vehicle Safety (CVS) system has attracted wide attention. As an important functional system of the CVS system, Lane Departure Warning system s function is to warn the driver when the vehicle changes the lane without the driver s intention. In this paper, we present a method of lane detection which based on Lane geometric model and image connected region process. The model of the lane is produced by the projection of camera, which makes the parallel lane lines in real world become intersect. The lines on the same side of lane have the same geometric feature that can be used to build lane line geometric model for detection. The lane line can be classified as different lane line models by the geometric feature of it. In this paper, due to the different features of lane line in different images, the operation and the classification of modeling performs at the same time. But before that, we go through some process to pre-process the image, such as the image connected region process, morphology tophat filter, etc. The combination of these modules can overcome the universal lane detection problems, such as the road in different light conditions. Experimental results on real road will be presented to prove the effectiveness of the proposed lane detection algorithm.