Open AccessImproved river flow and random sample consensus for curve lane detection
Author(s) -
Huachun Tan,
Yang Zhou,
Yong Zhu,
Danya Yao,
Jianqiang Wang
Publication year - 2015
Publication title -
advances in mechanical engineering/advances in mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 40
eISSN - 1687-8140
pISSN - 1687-8132
DOI - 10.1177/1687814015593866
Subject(s) - curvature , feature (linguistics) , computer science , artificial intelligence , sample (material) , computer vision , hyperbola , pattern recognition (psychology) , mathematics , geometry , philosophy , linguistics , chemistry , chromatography
Accurate and robust lane detection, especially the curve lane detection, is the premise of lane departure warning system and forward collision warning system. In this article, an algorithm based on improved river flow and random sample consensus is proposed to detect curve lane under challenging conditions including the dashed lane markings and vehicle occlusion. The curve lanes are modeled as hyperbola pair. To determine the coefficient of curvature, an improved river flow method is presented to search feature points in the far vision field guided by the results of detected straight lines in near vision field or the curved lines from the last frame, which can connect dashed lane markings or obscured lane markings. As a result, it is robust on dashed lane markings and vehicle occlusion conditions. Then, random sample consensus is utilized to calculate the curvature, which can eliminate noisy feature points obtained from improved river flow. The experimental results show that the proposed method can accurately detect lane under challenging conditions