Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Realtime Robust Curved Lane Detection Algorithm using Gaussian Mixture Model

가우시안 혼합모델을 이용한 강인한 실시간 곡선차선 검출 알고리즘

  • 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)
  • 장찬희 (한동대학교 기계제어공학부) ;
  • 이순주 (국방과학연구소) ;
  • 최창범 (한동대학교 창의융합교육원) ;
  • 김영근 (한동대학교 기계제어공학부)
  • Received : 2015.05.06
  • Accepted : 2015.12.21
  • Published : 2016.01.01
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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

References

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