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DriveLM: Driving with Graph Visual Question Answering.

We facilitate Perception, Prediction, Planning, Behavior, Motion tasks with human-written reasoning logic as a connection. We propose the task of GVQA to connect the QA pairs in a graph-style structure. To support this novel task, we provide the DriveLM-Data.

DriveLM-Data comprises two distinct components: DriveLM-nuScenes and DriveLM-CARLA. In the case of DriveLM-nuScenes, we construct our dataset based on the prevailing nuScenes dataset. As for DriveLM-CARLA, we collect data from the CARLA simulator.

Prepare DriveLM-nuScenes Dataset

Our DriveLM-nuScenes contains a collection of questions and answers. The dataset is named v1_0_train_nus.json. We offer a subset of image data that includes all the images used in our DriveLM. You can also download the full nuScenes dataset HERE.

Usage

  1. Download nuScenes subset image data (or full nuScenes dataset) and v1_0_train_nus.json.

  2. Organize the data structure as follows:

DriveLM
├── data/
│   ├── QA_dataset_nus/
│   │   ├── v1_0_train_nus.json
│   ├── nuscenes/
│   │   ├── samples/

Prepare DriveLM-CARLA Dataset

We provide a collection of questions and answers for DriveLM-CARLA. The dataset drivelm_carla_vqas.zip contains question-answer pairs for pre-determined keyframes listed in drivelm_carla_keyframes.txt for each scenario. You can download the full dataset using the provided script. All scripts for extracting keyframes and generating the question-answer dataset are available on GitHub. Additionally, we also provide instructions how to built your own dataset.

License and Citation

This language dataset is licensed under CC-BY-NC-SA 4.0. If you use this dataset, please cite our work:

@article{drivelm_paper2023,
  title={DriveLM: Driving with Graph Visual Question Answering},
  author={Sima, Chonghao and Renz, Katrin and Chitta, Kashyap and Chen, Li and Zhang, Hanxue and Xie, Chengen and Luo, Ping and Geiger, Andreas and Li, Hongyang},
  journal={arXiv preprint arXiv:2312.14150},
  year={2023}
}
@misc{drivelm_repo2023,
  title={DriveLM: Driving with Graph Visual Question Answering},
  author={DriveLM contributors},
  howpublished={\url{https://github.com/OpenDriveLab/DriveLM}},
  year={2023}
}

For more information and updates, please visit our GitHub repository.

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