Real-time lane detection for autonomous vehicle

Abstract

A lane detection based on a road model or feature needs correct acquisition of information on the lane in an image. It is inefficient to implement a lane detection algorithm through the full range of an image when it is applied to a real road in real time because of the calculation time. This paper defines two search ranges of detecting a lane in a road. First is the searching mode that searches the lane without any prior information of a road. Second is recognition mode, which is able to reduce the size and change the position of a searching range by predicting the position of a lane through the acquired information in a previous frame. It is allowed to extract accurately and efficiently the edge candidate points of a lane without any unnecessary searching. By means of inverse perspective transform that removes the perspective effect on the edge candidate points, we transform the edge candidate information in the image coordinate system (ICS) into the plane-view image in the world coordinate system (WCS). We define a linear approximation filter and remove faulty edge candidate points by using it. This paper aims to approximate more correctly the lane of an actual road by applying the least-mean square method with the fault-removed edge information for curve fitting.