Xiaolong Liu 刘晓龙

KITTI
KITTI-ROAD task

This benchmark has been created in collaboration with Jannik Fritsch and Tobias Kuehnl from Honda Research Institute Europe GmbH. The road and lane estimation benchmark consists of 289 training and 290 test images. It contains three different categories of road scenes:

  • uu - urban unmarked (98/100)
  • um - urban marked (95/96)
  • umm - urban multiple marked lanes (96/94)
  • urban - combination of the three above
News

Algorithm RPP once (2016.12.25-2017.2.6) won the first places on both unmarked road and marked road tasks, the fourth place in multiple marked lane task, and the second place in combination task, compared to all the other dozens of methods.
清华大学计算机系相关报道
Algorithm DFFA  once (2017.7.12-?) won the first places on both UM_LANE Lane Estimation Evaluation and Behaviour Evaluation tasks.