The fast lane detection of road using RANSAC algorithm

Abstract

In order to ensure driving safety and advanced driver assistance systems (ADAS) attracted more and more attention. Lane departure warning system is an important part of the system. Fast and stable lane detection is a prerequisite for Lane detection under complex background. In this paper, we propose a new lane detection method through a bird’s eye view maps and modified RANSAC (random sampling) based on inspiration from the road feature extraction algorithm for remote sensing images. According to the image of a bird’s eye view, we can identify the tag line through progressive probabilistic Hough transform in the opposite lane detection. Then the group rows are detected by a new weighting scheme based on distance, we can get a candidate lane field. Each field, Lane the RANSAC algorithm is improved and the dual-model fitting. Therefore, the curvature of the road direction can be predicted and the slope of the line. Finally, our results show that lane detection algorithm is robust and real-time performance in a variety of road conditions.