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 Dataset) OpenDV-YouTube 🔥
- DriveData Survey
- DriveLM 🔥
- OpenScene
- OpenLane-V2 Update
2024/05/28 We released our latest research, Vista, a generalizable driving world model. It's capable of predicting high-fidelity and long-horizon futures, executing multi-modal actions, and serving as a generalizable reward function to assess driving behaviors.
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.
Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability
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 - Repo: 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}
}
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 about3 TBstorage 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 TBstorage space in total.
- The downloaded raw videos (
- 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.
@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}
}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)
Challenges and Leaderboards
| Title | Host | Year | Task | Entry |
|---|---|---|---|---|
| Autonomous Driving Challenge | OpenDriveLab | CVPR2023 | Perception / OpenLane Topology | 111 |
| Perception / Online HD Map Construction | ||||
| Perception / 3D Occupancy Prediction | ||||
| Prediction & Planning / nuPlan Planning | ||||
| Waymo Open Dataset Challenges | Waymo | CVPR2023 | Perception / 2D Video Panoptic Segmentation | 35 |
| Perception / Pose Estimation | ||||
| Prediction / Motion Prediction | ||||
| Prediction / Sim Agents | ||||
| CVPR2022 | Prediction / Motion Prediction | 128 | ||
| Prediction / Occupancy and Flow Prediction | ||||
| Perception / 3D Semantic Segmentation | ||||
| Perception / 3D Camera-only Detection | ||||
| CVPR2021 | Prediction / Motion Prediction | 115 | ||
| Prediction / Interaction Prediction | ||||
| Perception / Real-time 3D Detection | ||||
| Perception / Real-time 2D Detection | ||||
| Argoverse Challenges | Argoverse | CVPR2023 | Prediction / Multi-agent Forecasting | 81 |
| Perception & Prediction / Unified Sensorbased Detection, Tracking, and Forecasting | ||||
| Perception / LiDAR Scene Flow | ||||
| Prediction / 3D Occupancy Forecasting | ||||
| CVPR2022 | Perception / 3D Object Detection | 81 | ||
| Prediction / Motion Forecasting | ||||
| Perception / Stereo Depth Estimation | ||||
| CVPR2021 | Perception / Stereo Depth Estimation | 368 | ||
| Prediction / Motion Forecasting | ||||
| Perception / Streaming 2D Detection | ||||
| CARLA Autonomous Driving Challenge | CARLA Team, Intel | 2023 | Planning / CARLA AD Challenge 2.0 | - |
| NeurIPS2022 | Planning / CARLA AD Challenge 1.0 | 19 | ||
| NeurIPS2021 | Planning / CARLA AD Challenge 1.0 | - | ||
| 粤港澳大湾区 （黄埔）国际算法算例大赛 | 琶洲实验室 | 2023 | 感知 / 跨场景单目深度估计 | - |
| 感知 / 路侧毫米波雷达标定和目标跟踪 | - | |||
| 2022 | 感知 / 路侧三维感知算法 | - | ||
| 感知 / 街景图像店面招牌文字识别 | - | |||
| AI Driving Olympics | ETH Zurich, University of Montreal,Motional | NeurIP2021 | Perception / nuScenes Panoptic | 11 |
| ICRA2021 | Perception / nuScenes Detection | 456 | ||
| Perception / nuScenes Tracking | ||||
| Prediction / nuScenes Prediction | ||||
| Perception / nuScenes LiDAR Segmentation | ||||
| 计图 (Jittor)人工智能算法挑战赛 | 国家自然科学基金委信息科学部 | 2021 | 感知 / 交通标志检测 | 37 |
| KITTI Vision Benchmark Suite | University of Tübingen | 2012 | Perception / Stereo, Flow, Scene Flow, Depth, Odometry, Object, Tracking, Road, Semantics | 5,610 |
Perception Datasets
| Dataset | Year | Diversity | Sensor | Annotation | Paper | ||||
|---|---|---|---|---|---|---|---|---|---|
| Scenes | Hours | Region | Camera | Lidar | Other | ||||
| KITTI | 2012 | 50 | 6 | EU | Font-view | ✗ | GPS & IMU | 2D BBox & 3D BBox | Link |
| Cityscapes | 2016 | - | - | EU | Font-view | ✗ | 2D Seg | Link | |
| Lost and Found | 2016 | 112 | - | - | Font-view | ✗ | 2D Seg | Link | |
| Mapillary | 2016 | - | - | Global | Street-view | ✗ | 2D Seg | Link | |
| DDD17 | 2017 | 36 | 12 | EU | Front-view | ✗ | GPS & CAN-bus & Event Camera | - | Link |
| Apolloscape | 2016 | 103 | 2.5 | AS | Front-view | ✗ | GPS & IMU | 3D BBox & 2D Seg | Link |
| BDD-X | 2018 | 6984 | 77 | NA | Front-view | ✗ | Language | Link | |
| HDD | 2018 | - | 104 | NA | Front-view | ✓ | GPS & IMU & CAN-bus | 2D BBox | Link |
| IDD | 2018 | 182 | - | AS | Front-view | ✗ | 2D Seg | Link | |
| SemanticKITTI | 2019 | 50 | 6 | EU | ✗ | ✓ | 3D Seg | Link | |
| Woodscape | 2019 | - | - | Global | 360° | ✓ | GPS & IMU & CAN-bus | 3D BBox & 2D Seg | Link |
| DrivingStereo | 2019 | 42 | - | AS | Front-view | ✓ | - | Link | |
| Brno-Urban | 2019 | 67 | 10 | EU | Front-view | ✓ | GPS & IMU & Infrared Camera | - | Link |
| A*3D | 2019 | - | 55 | AS | Front-view | ✓ | 3D BBox | Link | |
| Talk2Car | 2019 | 850 | 283.3 | NA | Front-view | ✓ | Language & 3D BBox | Link | |
| Talk2Nav | 2019 | 10714 | - | Sim | 360° | ✗ | Language | Link | |
| PIE | 2019 | - | 6 | NA | Front-view | ✗ | 2D BBox | Link | |
| UrbanLoco | 2019 | 13 | - | AS & NA | 360° | ✓ | IMU | - | Link |
| TITAN | 2019 | 700 | - | AS | Front-view | ✗ | 2D BBox | Link | |
| H3D | 2019 | 160 | 0.77 | NA | Front-view | ✓ | GPS & IMU | - | Link |
| A2D2 | 2020 | - | 5.6 | EU | 360° | ✓ | GPS & IMU & CAN-bus | 3D BBox & 2D Seg | Link |
| CARRADA | 2020 | 30 | 0.3 | NA | Front-view | ✗ | Radar | 3D BBox | Link |
| DAWN | 2019 | - | - | Global | Front-view | ✗ | 2D BBox | Link | |
| 4Seasons | 2019 | - | - | - | Front-view | ✗ | GPS & IMU | - | Link |
| UNDD | 2019 | - | - | - | Front-view | ✗ | 2D Seg | Link | |
| SemanticPOSS | 2020 | - | - | AS | ✗ | ✓ | GPS & IMU | 3D Seg | Link |
| Toronto-3D | 2020 | 4 | - | NA | ✗ | ✓ | 3D Seg | Link | |
| ROAD | 2021 | 22 | - | EU | Front-view | ✗ | 2D BBox & Topology | Link | |
| Reasonable Crowd | 2021 | - | - | Sim | Front-view | ✗ | Language | Link | |
| METEOR | 2021 | 1250 | 20.9 | AS | Front-view | ✗ | GPS | Language | Link |
| PandaSet | 2021 | 179 | - | NA | 360° | ✓ | GPS & IMU | 3D BBox | Link |
| MUAD | 2022 | - | - | Sim | 360° | ✓ | 2D Seg& 2D BBox | Link | |
| TAS-NIR | 2022 | - | - | - | Front-view | ✗ | Infrared Camera | 2D Seg | Link |
| LiDAR-CS | 2022 | 6 | - | Sim | ✗ | ✓ | 3D BBox | Link | |
| WildDash | 2022 | - | - | - | Front-view | ✗ | 2D Seg | Link | |
| OpenScene | 2023 | 1000 | 5.5 | AS & NA | 360° | ✗ | 3D Occ | Link | |
| ZOD | 2023 | 1473 | 8.2 | EU | 360° | ✓ | GPS & IMU & CAN-bus | 3D BBox & 2D Seg | Link |
| nuScenes | 2019 | 1000 | 5.5 | AS & NA | 360° | ✓ | GPS & CAN-bus & Radar & HDMap | 3D BBox & 3D Seg | Link |
| Argoverse V1 | 2019 | 324k | 320 | NA | 360° | ✓ | HDMap | 3D BBox & 3D Seg | Link |
| Waymo | 2019 | 1000 | 6.4 | NA | 360° | ✓ | 2D BBox & 3D BBox | Link | |
| KITTI-360 | 2020 | 366 | 2.5 | EU | 360° | ✓ | 3D BBox & 3D Seg | Link | |
| ONCE | 2021 | - | 144 | AS | 360° | ✓ | 3D BBox | Link | |
| nuPlan | 2021 | - | 120 | AS & NA | 360° | ✓ | 3D BBox | Link | |
| Argoverse V2 | 2022 | 1000 | 4 | NA | 360° | ✓ | HDMap | 3D BBox | Link |
| DriveLM | 2023 | 1000 | 5.5 | AS & NA | 360° | ✗ | Language | Link | |
Mapping Datasets
| Dataset | Year | Diversity | Sensor | Annotation | Paper | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Scenes | Frames | Camera | Lidar | Type | Space | Inst. | Track | |||
| Caltech Lanes | 2008 | 4 | 1224/1224 | ✗ | PV | ✓ | ✗ | Link | ||
| VPG | 2017 | - | 20K/20K | ✗ | PV | ✗ | - | Link | ||
| TUsimple | 2017 | 6.4K | 6.4K/128K | ✗ | PV | ✓ | ✗ | Link | ||
| CULane | 2018 | - | 133K/133K | ✗ | PV | ✓ | - | Link | ||
| ApolloScape | 2018 | 235 | 115K/115K | ✓ | PV | ✗ | ✗ | Link | ||
| LLAMAS | 2019 | 14 | 79K/100K | Front-view Image | ✗ | Laneline | PV | ✓ | ✗ | Link |
| 3D Synthetic | 2020 | - | 10K/10K | ✗ | PV | ✓ | - | Link | ||
| CurveLanes | 2020 | - | 150K/150K | ✗ | PV | ✓ | - | Link | ||
| VIL-100 | 2021 | 100 | 10K/10K | ✗ | PV | ✓ | ✗ | Link | ||
| OpenLane-V1 | 2022 | 1K | 200K/200K | ✗ | 3D | ✓ | ✓ | Link | ||
| ONCE-3DLane | 2022 | - | 211K/211K | ✗ | 3D | ✓ | - | Link | ||
| OpenLane-V2 | 2023 | 2K | 72K/72K | Multi-view Image | ✗ | Lane Centerline, Lane Segment | 3D | ✓ | ✓ | Link |
Prediction and Planning Datasets
| Subtask | Input | Output | Evaluation | Dataset |
|---|---|---|---|---|
| Motion Prediction | Surrounding Traffic States | Spatiotemporal Trajectories of Single/Multiple Vehicle(s) | Displacement Error | Argoverse |
| nuScenes | ||||
| Waymo | ||||
| Interaction | ||||
| MONA | ||||
| Trajectory Planning | Motion States for Ego Vehicles, Scenario Cognition and Prediction | Trajectories for Ego Vehicles | Displacement Error, Safety, Compliance, Comfort | nuPlan |
| CARLA | ||||
| MetaDrive | ||||
| Apollo | ||||
| Path Planning | Maps for Road Network | Routes Connecting to Nodes and Links | Efficiency, Energy Conservation | OpenStreetMap |
| Transportation Networks | ||||
| DTAlite | ||||
| PeMS | ||||
| New York City Taxi Data |
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