Road Detection Using Fisheye Camera and Laser Range Finder

Road detection is a significant task for the development of intelligent vehicles as well as advanced driver assistance systems (ADAS). For the past decade, many methods have been proposed. Among these approaches, one of them uses log-chromaticity space based illumination invariant grayscale image. However, errors in road detection could occur due to over saturation or under saturation, especially in weak lighting situations. In this paper, a new approach is proposed. It combines fisheye image information (in log-chromaticity space and in Lab color space) and laser range finder (LRF) measurements. Firstly, road is coarsely detected by a classifier based on the histogram of the illumination invariant grayscale image and a predefined road area. This fisheye image based coarse road detection is then faced to LRF measurements in order to detect eventual conflicts. Possible errors in coarse road detection can then be highlighted. Finally, in case of detected conflicts, a refined process based on Lab color space is carried out to rule out the errors. Experimental results based on real road scenes show the effectiveness of the proposed method.