A2D2: Audi Autonomous Driving Dataset

By Jakob Geyer, Y. Kassahun, Mentar Mahmudi, Xavier Ricou, R. Durgesh, Andrew S. Chung, L. Hauswald, Viet Hoang Pham, Maximilian Mühlegg, S. Dorn and 9 more. arXiv.org, 2020

485 citations recorded by the source corpus.

Research in machine learning, mobile robotics, and autonomous driving is accelerated by the availability of high quality annotated data. To this end, we release the Audi Autonomous Driving Dataset (A2D2). Our dataset consists of simultaneously recorded images and 3D point clouds, together with 3D bounding boxes, semantic segmentation, instance segmentation, and data extracted from the automotive bus. Our sensor suite consists of six cameras and five LiDAR units, providing full 360 degree coverage. The recorded data is time synchronized and mutually registered. Annotations are for non-sequential frames: 41,277 frames with semantic segmentation image and point cloud labels, of which 12,497 frames also have 3D bounding box annotations for objects within the field of view of the front camera. In addition, we provide 392,556 sequential frames of unannotated sensor data for recordings in three cities in the south of Germany. These sequences contain several loops. Faces and vehicle number plates are blurred due to GDPR legislation and to preserve anonymity. A2D2 is made available under the CC BY-ND 4.0 license, permitting commercial use subject to the terms of the license. Data and further information are available at http://www.a2d2.audi.

Subfields: Computer Vision and Pattern Recognition, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/07e3db9c2034b522c3cf04399602cc7124964306

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Citations in the corpus

RELLIS-3D Dataset: Data, Benchmarks and Analysis (2020, unknown): “However, RGB cameras are not the only sensors used in autonomous driving, therefore several multi-modal datasets have also been published, e.g., KITTI [21], nuScenes[11] and A2D2 [22].”
Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting (2023, methodology): “Many datasets are annotated “sensor” datasets [4, 45, 39, 40, 24, 33, 18, 14, 41, 36] in the spirit of the inﬂuential KITTI dataset [17].”
A Survey on Deep Learning Techniques for Stereo-Based Depth Estimation (2020, methodology): “Other datasets such as the ApolloScape [34] and A2D2 [35] contain very high resolution images, of the order of 3130 × 960 , with more that 100+ hours of stereo driving videos, in the case of ApolloScape, have been speciﬁcally designed to test autonomous driving algorithms.”
UniDepth: Universal Monocular Metric Depth Estimation (2024, methodology): “The training dataset utilized is the ensemble of Argoverse2 [43], Waymo [39], Driv-ingStereo [45], Cityscapes [6], BDD100K [49], Mapillary-PSD [1], A2D2 [18], ScanNet [7], and Taskonomy [51].”
KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D (2021, background): “A2D2 [36] labels 2D images and maps 2D labels to 3D to obtain per-point 3D labels.”
IDDA: A Large-Scale Multi-Domain Dataset for Autonomous Driving (2020, background): “For this purpose we use the same networks from the first experiment but test them on Cityscapes, BDD100K, Mapillary Vistas and A2D2.”
Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, background): “Some automobile manufacturers publish datasets collected by their vehicles, including H3D [128], A2D2 [129] and the Ford Dataset [130].”
DSEC: A Stereo Event Camera Dataset for Driving Scenarios (2021, background): “Since then, a number of large scale datasets for semantic segmentation have been released [21], [22], [23], [24].”
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives (2023, background): “ai [143], Bdd100K [149], A2D2 [150], Automine [151], DriverTruth [155] and Sups [154], most of the famous dataset is shown in Table IV.”