This is "The One" project that OpenDriveLab is committed to contribute to the community, providing some thought and general picture of how to embrace foundation models into autonomous driving.

• NEWS
• At A Glance
• 🚀 Vista
• ⭐ GenAD: OpenDV Dataset (CVPR'24 Hightlight)
• ⭐ DriveLM (ECCV'24 Oral)
• DriveData Survey
• OpenScene
• OpenLane-V2 Update

Quick facts:

• Introducing the world's first generalizable driving world model.
• Task: High-fidelity, action-conditioned, and long-horizon future prediction for driving scenes in the wild.
• Dataset: OpenDV-YouTube, nuScenes
• Code and model: https://github.com/OpenDriveLab/Vista
• Video Demo: https://vista-demo.github.io
• Related work: Vista, GenAD

@article{gao2024vista,
 title={Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability}, 
 author={Shenyuan Gao and Jiazhi Yang and Li Chen and Kashyap Chitta and Yihang Qiu and Andreas Geiger and Jun Zhang and Hongyang Li},
 journal={arXiv preprint arXiv:2405.17398},
 year={2024}
}

@inproceedings{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},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2024}
}

🎦 The Largest Driving Video dataset to date, containing more than 1700 hours of real-world driving videos and being 300 times larger than the widely used nuScenes dataset.

• Complete video list (under YouTube license): OpenDV Videos.
  • The downloaded raw videos (mostly 1080P) consumes about 3 TB storage space. However, these hour-long videos cannot be directly applied for model training as they are extremely memory consuming.
  • Therefore, we preprocess them into conseductive images which are more flexible and efficient to load during training. Processed images consumes about 24 TB storage space in total.
  • It's recommended to set up your experiments on a small subset, say 1/20 of the whole dataset. After stablizing the training, you can then apply your method on the whole dataset and hope for the best 🤞.
• Step-by-step instruction for data preparation: OpenDV-YouTube.
• Language annotation for OpenDV-YouTube: OpenDV-YouTube-Language.

Quick facts:

• Task: large-scale video prediction for driving scenes.
• Data source: YouTube, with careful collection and filtering process.
• Diversity Highlights: 1700 hours of driving videos, covering more than 244 cities in 40 countries.
• Related work: GenAD Accepted at CVPR 2024, Highlight
• Note: Annotations for other public datasets in OpenDV-2K will not be released since we randomly sampled a subset of them in training, which are incomplete and hard to trace back to their origins (i.e., file name). Nevertheless, it's easy to reproduce the collection and annotation process on your own following our paper.

@inproceedings{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},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2024}
}

Introducing the First benchmark on Language Prompt for Driving.

Quick facts:

• Task: given the language prompts as input, predict the trajectory in the scene
• Origin dataset: nuScenes, CARLA (To be released)
• Repo: https://github.com/OpenDriveLab/DriveLM, https://github.com/OpenDriveLab/ELM
• Related work: DriveLM, ELM
• Related challenge: Driving with Language AGC Challenge 2024

@article{li2024_driving_dataset_survey,
 title = {Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future},
 author = {Hongyang Li and Yang Li and Huijie Wang and Jia Zeng and Huilin Xu and Pinlong Cai and Li Chen and Junchi Yan and Feng Xu and Lu Xiong and Jingdong Wang and Futang Zhu and Chunjing Xu and Tiancai Wang and Fei Xia and Beipeng Mu and Zhihui Peng and Dahua Lin and Yu Qiao},
 journal = {SCIENTIA SINICA Informationis},
 year = {2024},
 doi = {10.1360/SSI-2023-0313}
}