Inverse perspective mapping for real-time Lane Detection in City Streets

Lane detection is a crucial step for any autonomous driving system to decrease car accidents and increase safety. In this paper, based on inverse perspective mapping and Probabilistic Hough Transform, we propose a lane detection system which works on city street images. First, by using inverse perspective mapping the top view of the street is obtained. Second, the lanes are rectified using a specifically designed filter which enhances the lanes and suppresses other elements. Then, by using Probabilistic Hough transform the location of the lanes is detected in the images. For the final refinement, lane candidates are mapped to the road image using perspective mapping and the lane intensity is analyzed to reduce false acceptance. We evaluate the performance of the proposed method on Caltech-lane dataset and the obtained results show that the proposed method is able to detect straight lanes.

Inverse perspective mapping for real-time Lane Detection in City Streets Keywords:

lane detection , lane model , autonomous driving , inverse perspective mapping , Probabilistic Hough Transform