Robust road lane detection from shape and color feature fusion for vehicle self-localization

Vehicle self-localization plays critical role on safety driving assistant and automatic driving. Vehicle self-localization can be applied to Lane Departure Warning Systems (LDWS) in order to provide higher vehicle safety and is very useful in driving behavior research. This paper presents a lane detection method by integrating lane shape and color features as well as using the detected lane for online vehicle position calculation. First, a statistical color model, called Gaussian Statistical Color Model (G-SCM) is implemented for image processing to extract the region of interest and to eliminate the interference of the lane. Moreover, it is to detect the lane and get the vanishing point in the direction of the lane by utilizing the improved Hough transform on the selected image area. In the last step, we can calculate the vehicle position (yaw angle of vehicle to the lane and the distance between the vehicle and the detected lane) by the corresponding relation of the image coordinate system and the world coordinate system. Experimental results show that this method can quickly and accurately detect the lane and complete a remarkable vehicle position.