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
Ground truth has been generated by manual annotation of the images and is available for two different road terrain types: road - the road area, i.e, the composition of all lanes, and lane - the ego-lane, i.e., the lane the vehicle is currently driving on (only available for category "um"). Ground truth is provided for training images only.
We evaluate road and lane estimation performance in the bird's-eye-view space. For the classical pixel-based evaluation we use established measures as discussed in our ITSC 2013 publication. MaxF: Maximum F1-measure, AP: Average precision as used in PASCAL VOC challenges, PRE: Precision, REC: Recall, FPR: False Positive Rate, FNR: False Negative Rate (the four latter measures are evaluated at the working point MaxF), F1: F1 score, HR: Hit rate. For the novel behavior-based evaluation a corridor with the vehicle width (2.2m) is fitted to the lane estimation processing result and evaluation is performed for 3 different distance values: 20 m, 30 m, and 40 m. We refer to our ITSC 2013 publication for more details. IMPORTANT NOTE: On 09.02.2015 we have improved the accuracy of the ground truth and re-calculated the results for all methods. Please download the devkit and the dataset with the improved ground truth for training again, if you have downloaded the files prior to 09.02.2015. Please consider reporting these new number for all future submissions. The last leaderboards right before the changes can be found here!
Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.
Additional information used by the methods
Stereo: Method uses left and right (stereo) images
Laser Points: Method uses point clouds from Velodyne laser scanner
GPS: Method uses GPS information
Additional training data: Use of additional data sources for training (see details)
ROMA (ROad MArkings): Dataset for performance evaluation of road marking extraction algorithms.
Citation
When using this dataset in your research, we will be happy if you cite us:
@INPROCEEDINGS{Fritsch2013ITSC,
author = {Jannik Fritsch and Tobias Kuehnl and Andreas Geiger},
title = {A New Performance Measure and Evaluation Benchmark for Road Detection Algorithms}, booktitle = {International Conference on Intelligent Transportation Systems (ITSC)},
year = {2013}
}
Stereo: Method uses left and right (stereo) images
Laser Points: Method uses point clouds from Velodyne laser scanner
GPS: Method uses GPS information
Additional training data: Use of additional data sources for training (see details)