A2D2: Audi Autonomous Driving Dataset

TL;DR

The A2D2 dataset provides high-quality, synchronized multi-modal sensor data with extensive annotations for autonomous driving research, facilitating advancements in machine learning and robotics.

Contribution

This paper introduces the comprehensive A2D2 dataset with synchronized images, point clouds, and detailed annotations, filling a gap in available autonomous driving datasets.

Findings

Contains over 41,000 annotated frames with semantic and 3D object labels

Includes nearly 400,000 unannotated frames for diverse training scenarios

Provides full 360-degree sensor coverage with synchronized data

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 http://www.a2d2.audi.