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http://dx.doi.org/10.5302/J.ICROS.2016.15.0073

Realtime Robust Curved Lane Detection Algorithm using Gaussian Mixture Model  

Jang, Chanhee (School of Mechanical and Control Engineering, Handong University)
Lee, Sunju (Modeling and Simulation Division, Agency for Defense Development)
Choi, Changbeom (School of Creative Convergence Education, Handong University)
Kim, Young-Keun (School of Mechanical and Control Engineering, Handong University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.22, no.1, 2016 , pp. 1-7 More about this Journal
Abstract
ADAS (Advanced Driver Assistance Systems) requires not only real-time robust lane detection, both straight and curved, but also predicting upcoming steering direction by detecting the curvature of lanes. In this paper, a curvature lane detection algorithm is proposed to enhance the accuracy and detection rate based on using inverse perspective images and Gaussian Mixture Model (GMM) to segment the lanes from the background under various illumination condition. To increase the speed and accuracy of the lane detection, this paper used template matching, RANSAC and proposed post processing method. Through experiments, it is validated that the proposed algorithm can detect both straight and curved lanes as well as predicting the upcoming direction with 92.95% of detection accuracy and 50fps speed.
Keywords
curve lane detection; lane departure warning; advanced driver assistance systems;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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