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license: cc-by-nc-sa-4.0
viewer: false
---
# **DriveLM:** Driving with **G**raph **V**isual **Q**uestion **A**nswering.
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](https://www.nuscenes.org/download).
## 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](https://github.com/OpenDriveLab/DriveLM/blob/DriveLM-CARLA/vqa_dataset/download_pdm_lite_carla_lb2.py). All scripts for extracting keyframes and generating the question-answer dataset are available on [GitHub](https://github.com/OpenDriveLab/DriveLM/blob/DriveLM-CARLA/vqa_dataset). 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](https://creativecommons.org/licenses/by-nc-sa/4.0/). If you use this dataset, please cite our work:
```BibTeX
@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}
}
```
```BibTeX
@misc{drivelm_repo2023,
title={DriveLM: Driving with Graph Visual Question Answering},
author={DriveLM contributors},
howpublished={\url{https://github.com/OpenDriveLab/DriveLM}},
year={2023}
}
```
Paper dataset for ECCV 2024 [DriveLM: Driving with Graph Visual Question Answering](https://huggingface.co/papers/2312.14150)
For more information and updates, please visit our [GitHub repository](https://github.com/OpenDriveLab/DriveLM).
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