Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Real Time Road Lane Detection with RANSAC and HSV Color Transformation

  • Kim, Kwang Baek (Division of Computer Software Engineering, Silla University) ;
  • Song, Doo Heon (Department of Computer Games, Yong-In SongDam College)
  • Received : 2017.08.21
  • Accepted : 2017.09.18
  • Published : 2017.09.30
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Abstract

Autonomous driving vehicle research demands complex road and lane understanding such as lane departure warning, adaptive cruise control, lane keeping and centering, lane change and turn assist, and driving under complex road conditions. A fast and robust road lane detection subsystem is a basic but important building block for this type of research. In this paper, we propose a method that performs road lane detection from black box input. The proposed system applies Random Sample Consensus to find the best model of road lanes passing through divided regions of the input image under HSV color model. HSV color model is chosen since it explicitly separates chromaticity and luminosity and the narrower hue distribution greatly assists in later segmentation of the frames by limiting color saturation. The implemented method was successful in lane detection on real world on-board testing, exhibiting 86.21% accuracy with 4.3% standard deviation in real time.

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References

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