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.
Here are some key components to construct a large foundation model curated for an autonomous system.
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
@misc{li2023opensourced, 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 Pinlong Cai and Huilin Xu and Dahua Lin and Junchi Yan and Feng Xu and Lu Xiong and Jingdong Wang and Futang Zhu and Kai Yan and Chunjing Xu and Tiancai Wang and > Beipeng Mu and Shaoqing Ren and Zhihui Peng and Yu Qiao}, year={2023}, eprint={2312.03408}, archivePrefix={arXiv}, primaryClass={cs.CV} }
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 - Repo: https://github.com/OpenDriveLab/DriveLM
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, 3D Occupancy Prediction Challenge 2023
Flourishing OpenLane-V2 with Standard Definition (SD) Map and Scene Elements.
Quick facts:
- Task: given SD-map (also known as ADAS map) and scene elements as input, build the driving scene on the fly without aid of HD-map.
- Repo: https://github.com/OpenDriveLab/OpenLane-V2
- Related work: TopoNet, Lane Topology Challenge 2023
