Accurate Lane Detection for Self-Driving Cars: An Approach Based on Color Filter Adjustment and K-Means Clustering Filter Article (Web of Science)

abstract

  • Lane detection is a crucial factor for self-driving cars to achieve a fully autonomous mode. Due to its importance, lane detection has drawn wide attention in recent years for autonomous driving. One challenge for accurate lane detection is to deal with noise appearing in the input image, such as object shadows, brake marks, breaking lane lines. To address this challenge, we propose an effective road detection algorithm. We leverage the strength of color filters to find a rough localization of the lane marks and employ a K-means clustering filter to screen out the embedded noises. We use an extensive experiment to verify the effectiveness of our method. The result indicates that our approach is robust to process noises appearing in input image, which improves the accuracy in lane detection.

authors

  • Liu, Dongfang
  • Wang, Yaqin
  • Chen, Tian
  • Matson, Eric T.

publication date

  • 2020

number of pages

  • 15

start page

  • 153

end page

  • 168

volume

  • 14

issue

  • 01