文献类型：期刊文献

英文题名：Robust Lane-Mark Extraction for Autonomous Driving under Complex Real Conditions

作者：Xuan, Hanyu[1]; Liu, Hongzhe[1]; Yuan, Jiazheng[2]; Li, Qing[3]

第一作者：Xuan, Hanyu

通讯作者：Liu, Hongzhe

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China; [2] Information Institute of Beijing Union University, Beijing, 100101, China; [3] Engineering Center of Beijing Union University, Beijing, 100101, China

第一机构：北京联合大学北京市信息服务工程重点实验室

年份：2017

卷号：6

起止页码：5749-5765

外文期刊名：IEEE Access

收录：EI(收录号：20173704147944)

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61571045 and Grant 61372148, in part by the Beijing Natural Science Foundation under Grant 4152016, and in part by the High-level Teacher Team Building Support Plan of Beijing municipal colleges and universities—High-level Innovative Team Building Plan under Grant IDHT20170511.

语种：英文

外文关键词：Advanced driver assistance systems - Autonomous vehicles - Cameras - Cognitive systems - Extraction - Image processing - Intelligent systems - Median filters - Roads and streets

摘要：Lane marks on roads are among the most important items of road scene information in the process of autonomous driving, and lane-mark extraction based on visual cognitive computing is one of the most important components of advanced driving assistance systems in intelligent transportation system. Onboard cameras mounted on the front of autonomous vehicles capture road scene images from which lane marks are extracted. This paper proposes a new lane-mark extraction algorithm with four major parts. First, this paper handles the road images captured from onboard cameras by grayscale and fast median filter. Then, we exploit the characteristics of lane marks in road images as constraints to propose lane-features filter based on multi-constraints used to extract lane marks. Then, a clustering algorithm based on the double point removal of a p -least squares algorithm is proposed to cluster features, and recursive dichotomy algorithm is used to fit the candidate lane marks. Finally, we carry out verification and optimization on candidate lane marks to obtain more accurate and stable extraction results. In our experiment, we divide the common complex road scenes into four categories. The results show that the proposed method can robustly extract lane marks under various complex real conditions. This paper also proposes forward a method to evaluate the results of lane-mark extraction, and partial test results are evaluated. ? 2013 IEEE.