aiqbench analysis
The release of the Audi Autonomous Driving Dataset (A2D2) provides a critical resource for advancing machine learning models in autonomous driving by offering synchronized multimodal sensor data, including images and LiDAR point clouds. This high-quality dataset enables researchers to train algorithms for tasks such as 3D object detection and semantic segmentation under real-world conditions across multiple urban environments.
A2D2: Audi Autonomous Driving Dataset
arXiv:2004.06320v2 Announce Type: replace-cross Abstract: 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 https://a2d2-dataset.github.io/.
Authors:Jakob Geyer, Yohannes Kassahun, Mentar Mahmudi, Xavier Ricou, Rupesh Durgesh, Andrew S. Chung, Lorenz Hauswald, Viet Hoang Pham, Maximilian Mühlegg, Sebastian Dorn, Tiffany Fernandez, Martin Jänicke, Sudesh Mirashi, Chiragkumar Savani, Martin Sturm, Oleksandr Vorobiov, Martin Oelker, Sebastian Garreis, Peter Schuberth
Abstract: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 this https URL.
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
arXiv:2004.06320 [cs.CV]
(or arXiv:2004.06320v2 [cs.CV] for this version)
Full text under the publisher's open licence · 251 words
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