OpenDriveLab/OpenDV-YouTube-Language

OpenDV-YouTube

This is the dataset repository of OpenDV-YouTube language annotations, including context and command. For more details, please refer to GenAD project and OpenDV-YouTube.

Usage

To use the annotations, you need to first download and prepare the data as instructed in OpenDV-YouTube. Note that we recommend to process the dataset in Linux environment since Windows may have issues with the file paths.

You can use the following code to load in annotations respectively.

import json

# for train
full_annos = []
for split_id in range(10):
  split = json.load(open("10hz_YouTube_train_split{}.json".format(str(split_id)), "r"))
  full_annos.extend(split)

# for val
val_annos = json.load(open("10hz_YouTube_val.json", "r"))

Annotations will be loaded in full_annos as a list where each element contains annotations for one video clip. All elements in the list are dictionaries of the following structure.

{
  "cmd": <int> -- command, i.e. the command of the ego vehicle in the video clip.
  "blip": <str> -- context, i.e. the BLIP description of the center frame in the video clip.
  "folder": <str> -- the relative path from the processed OpenDV-YouTube dataset root to the image folder of the video clip.
  "first_frame": <str> -- the filename of the first frame in the clip. Note that this file is included in the video clip.
  "last_frame": <str> -- the filename of the last frame in the clip. Note that this file is included in the video clip.
}

The command, i.e. the cmd field, can be converted to natural language using the map_category_to_caption function. You may refer to cmd2caption.py for details.

The context, i.e. the blip field, is the description of the center frame in the video generated by BLIP2.

Citation

If you find our work helpful, please cite the following paper.

@misc{yang2024genad,
      title={Generalized Predictive Model for Autonomous Driving}, 
      author={Jiazhi Yang and Shenyuan Gao and Yihang Qiu and Li Chen and Tianyu Li and Bo Dai and Kashyap Chitta and Penghao Wu and Jia Zeng and Ping Luo and Jun Zhang and Andreas Geiger and Yu Qiao and Hongyang Li},
      year={2024},
      eprint={2403.09630},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}