An illumination robust road detection method based on color names and geometric information. (December 2018)

• Record Type: Journal Article
• Authors: Xu, Fenglei; Hu, Bingwen; Chen, Lineng; Wang, Huan; Xia, Qingyuan; Sehdev, Paramjit; Ren, Mingwu
• Is Part Of: Cognitive systems research. Volume 52(2018)
• Page Start: 240
• Page End: 250
• Publication Date: 2018-12
• Subjects: Road detection -- Illumination robust -- Color names -- Vanishing point; Cognition -- Periodicals; Cognitive engineering (System design) -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1016/j.cogsys.2018.06.019
• Languages: English
• ISSNs: 1389-0417

Abstract

Abstract: Road detection is a basic task in automated driving field. In existing methods, detecting lane marks is a frequently used approach. However, road marks could fade with time, or even do not exist on some roads. Considering these factors, road region segmentation from background is much more reliable. One of the difficulties in this area is dealing with variant illumination conditions. Existing methods make some progress on reducing the influence of shadows, but they are still not satisfactory. Taking this into account, we put forward an improved shadow-free road detecting method based on color names, whose performance exceeds the existing methods. In addition, to further improving our performance on application, we adopt vanishing point detection and fuse two confidence maps to reduce the interference of sidewalk regions. Experiments on KITTI dataset depict that the proposed method is efficient. Moreover, the improved road detection method has low-complexity, which meets the requirement of practical usage.