We release the dataset with which we trained our leaderboard submission.
This dataset is converted from the original lmdb format.
If you find our dataset to be useful, please cite us as
@inproceedings{chen2021learning,
title={Learning to drive from a world on rails},
author={Chen, Dian and Koltun, Vladlen and Kr{\"a}henb{\"u}hl, Philipp},
booktitle={ICCV},
year={2021}
}
The dataset consists of 1M simulator frames.
Each frame contains the corresponding location, rotation, speed, the surrounding map, as well as three camera suite readings.
The images and semantic segmentation labels are stored in the rgbs folder of each trajectory folder.
Locations, rotations, speeds, and the high-level navigation commands, and paths to sensor readings are stored
in the data.json file inside each trajectory folder.
wide_{d}_{:05d}.jpgis the RGB image stitched from three frontal cameras with FOV of 60.
narr_{d}_{:05d}.jpgis the RGB image from the frontal cameras with FOV of 50.
The semantic segmentation labeled are stored as palette PNG files. Refer to this doc for reference.
wide_sem_{d}_{:05d}.pngis the corresponding semantic segmentation labels towide_{d}_{:05d}.jpg
narr_sem_{d}_{:05d}.pngis the corresponding semantic segmentation labels tonarr_{d}_{:05d}.jpg
The maps are stored as per channel PNG files as
lbl_{d}_{:05d}.pngwhere {d} is the channel number. If corresponds to
0: road mask
1: lane boundary mask
2: stop sign trigger box
3: vehicle
4: pedestrian
5: red light trigger box
6+:lane centers
The pre-labeled Q values are stoded in data.json files as a flattened list.
Its original shape is num_cmds x num_actions x num_speeds.
Q: Why are the images distorted?
A: Because leaderboard assumes lens distortion.
This dataset is released under the CC-BY license.
CARLA itself is under MIT license and its assets are under CC-BY license.