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
• (GenAD Dataset) OpenDV-YouTube 🔥
• DriveData Survey
  • Abstract
  • Related Work Collection
• DriveLM 🔥
• OpenScene
• OpenLane-V2 Update

2024/03/24 OpenDV-YouTube Update: Full suite of toolkits for OpenDV-YouTube is now available, including data downloading and processing scripts, as well as language annotations. Please refer to OpenDV-YouTube.

2024/03/15 We released the complete video list of OpenDV-YouTube, a large-scale driving video dataset, for GenAD project. Data downloading and processing script, as well as language annotations, will be released next week. Stay tuned.

2024/01/24 We are excited to announce some update to our survey and would like to thank John Lambert, Klemens Esterle from the public community for their advice to improve the manuscript.

Here are some key components to construct a large foundation model curated for an autonomous system.

Below we would like to share the latest update from our team on the DriveData side. We will release the detail of the DriveEngine and the DriveAGI in the future.

Generalized Predictive Model for Autonomous Driving (CVPR 2024, Highlight)

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): google sheet link
  • 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 process them into conseductive images which are more flexible and efficient to load during training. Processed images consumes about 24 TB storage space in total.
• Downloading and processing script: Please refer to OpenDV-YouTube.
• Language annotation for OpenDV-YouTube: You can download full annotations for OpenDV-YouTube at 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.

@article{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},
  journal={arXiv preprint arXiv:2403.09630},
  year={2024}
}

With the continuous maturation and application of autonomous driving technology, a systematic examination of open-source autonomous driving datasets becomes instrumental in fostering the robust evolution of the industry ecosystem. In this survey, we provide a comprehensive analysis of more than 70 papers on the timeline, impact, challenges, and future trends in autonomous driving dataset.

Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future

• English Version
• Chinese Version Accepted at SCIENTIA SINICA Informationis (中文版)

@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}
}

Current autonomous driving datasets can broadly be categorized into two generations since the 2010s. We define the Impact (y-axis) of a dataset based on sensor configuration, input modality, task category, data scale, ecosystem, etc.

We present comprehensive paper collections, leaderboards, and challenges.(Click to expand)

Below we would like to share the latest update from our team on the DriveData side. We will release the detail of the DriveEngine and the DriveAGI in the future.

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

The Largest up-to-date 3D Occupancy Forecasting dataset for visual pre-training.

Quick facts:

• Task: given the large amount of data, predict the 3D occupancy in the environment.
• Origin dataset: nuPlan
• Repo: https://github.com/OpenDriveLab/OpenScene
• Related work: OccNet
• Related challenge: 3D Occupancy Prediction Challenge 2023, Occupancy and Flow AGC Challenge 2024, Predictive World Model AGC Challenge 2024

Flourishing OpenLane-V2 with Standard Definition (SD) Map and Map Elements.

Quick facts:

• Task: given multi-view images and SD-map (also known as ADAS map) as input, build the driving scene on the fly without the aid of HD-map.
• Repo: https://github.com/OpenDriveLab/OpenLane-V2
• Related work: OpenLane-V2, TopoNet, LaneSegNet
• Related challenge: Lane Topology Challenge 2023, Mapless Driving AGC Challenge 2024