A2D2: Audi Multi-Sensor Autonomous Driving Data with Semantic Labels

An open multi-sensor dataset for autonomous driving research created by Audi AG. It contains semantically segmented images, semantic point clouds, and 3D bounding boxes, along with unlabeled 360-degree camera images, lidar, and bus data for three sequences. The dataset is hosted on AWS Open Data and is licensed under CC-BY-ND-4.0.

Use Cases

• Train semantic segmentation models based on the semantically segmented images.
• Develop 3D object detection algorithms based on the provided 3D bounding boxes.
• Fuse multi-sensor data for scene reconstruction based on the synchronized camera, lidar, and bus data.
• Conduct research on sensor fusion and self-supervised learning based on the unlabeled 360-degree camera and lidar sequences.

Strengths

• Includes multiple synchronized sensor modalities: cameras, lidar, and vehicle bus data.
• Provides high-level semantic labels for images and point clouds, including 3D bounding boxes.
• Contains three sequences of unlabeled data, which may support self-supervised learning tasks.

Limitations

• Row count and total dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Audi AG

Collection Method

null

Time Range

null

Freshness

null

Geography

null