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A2D2
2D Box
3D Semantic Segmentation
3D Box
2D Polygon
3D Instance Segmentation
Autonomous Driving
|...
License: CC BY-ND 4.0

Overview

We have published the Audi Autonomous Driving Dataset (A2D2) to support startups and academic
researchers working on autonomous driving. Equipping a vehicle with a multimodal sensor suite,
recording a large dataset, and labelling it, is time and labour intensive. Our dataset removes
this high entry barrier and frees researchers and developers to focus on developing new technologies
instead. The dataset features 2D semantic segmentation, 3D point clouds, 3D bounding boxes, and
vehicle bus data.

Data Collection

Sensor setup

Our sensor suite consists of six cameras, five LiDAR sensors, and an automotive gateway for
recording bus data. This configuration provides 360° coverage of the environment with camera
and LiDAR. The bus data give information about vehicle state and driver control input.

Sensors

Five LiDAR sensors

  • Up to 100 m range
  • +/- 3 cm accuracy
  • 16 channels
  • 10 Hz rotation rate
  • 360° horizontal field of view
  • +/- 15° vertical field of view

Front centre camera

  • 1920 × 1208 resolution
  • 60° horizontal field of view
  • 38° vertical field of view
  • 30 fps framerate

Surround cameras (5x)

  • 1920 × 1208 resolution
  • 120° horizontal view angle
  • 73° vertical view angle
  • 30 fps framerate

Bus gateway

  • Connected to built-in car gateway
  • Connection to all car buses and their sensors
  • Timestamping and forwarding of sensor data via Ethernet

Overview of sensor carrier with sensors (top view)Overview of sensor
carrier with sensors (top view)

Other hardware

Our vehicle is equipped with additional hardware for recording
data from the sensor suite and vehicle bus. The cameras are connected to an embedded computer
via LVDS, while the LiDAR sensors are connected via a 1G-Ethernet switch. Each LiDAR sensor
is connected to a GNSS receiver which acts as a clock. A further GNSS clock serves as a time
master for the gateway and embedded computer. The bus gateway connects to the embedded computer
via 1G-Ethernet. All data is stored on a crash-safe network storage device, equipped with 48
TB of SSD storage, and accessed via 10G-Ethernet.

Overview of the recording hardware and its setupOverview
of the recording hardware and its setup

Sensor synchronization

All sensor signals are timestamped in UTC format.
Camera images are timestamped when they arrive at the embedded computer, which is synchronised
to the time master. Bus data are timestamped at the gateway, which is also synchronised to
the time master. LiDAR signals are timestamped at the sensors, which get their time from GNSS.

Calibration

LiDAR-to-Vehicle

The LiDAR sensor pose relative to the vehicle is determined by direct measurement of positions
and orientation when mounted on the vehicle.

Camera-to-Vehicle

Camera poses with respect to the vehicle are
determined by direct in-situ measurements of position and orientation.

LiDAR-to-LiDAR optimization

We use one LiDAR as a reference and initialise the other LiDAR sensor poses to their measured
positions and orientations. Next, an Iterative Closest Point algorithm is used to refine the
poses of the other LiDAR sensors within the vehicle coordinate system. This registration
uses a recording of a static environment with a static ego vehicle and does not require any
fiducial targets.

Camera-to-LiDAR optimization

The camera poses are
optimized using camera and LiDAR recordings of fiducial targets (e.g. checkerboards). Additionally
a low speed driving scene is used to improve calibration of sensor orientation. This process
uses features (e.g. edges) in camera and LiDAR data to optimize relative poses.

Data Annotation

Semantic segmentation

The dataset features 41,280 frames with semantic segmentation in 38 categories. Each pixel
in an image is given a label describing the type of object it represents, e.g. pedestrian,
car, vegetation, etc.

Point cloud segmentation

Point cloud segmentation

Point cloud segmentation is produced
by fusing semantic pixel information and LiDAR point clouds. Each 3D point is thereby assigned
an object type label. This relies on accurate camera-LiDAR registration.

3D bounding boxes

3D bounding boxes

c3D bounding boxes are provided for 12,499 frames.
LiDAR points within the field of view of the front camera are labelled with 3D bounding boxes.
We annotate 14 classes relevant to driving, e.g. cars, pedestrians, buses, etc.

3D bounding boxes

Citation

Please use the following citation when referencing the dataset:

@article{geyer2020a2d2,
  title={A2d2: Audi autonomous driving dataset},
  author={Geyer, Jakob and Kassahun, Yohannes and Mahmudi, Mentar and Ricou, Xavier and Durgesh,
Rupesh and Chung, Andrew S and Hauswald, Lorenz and Pham, Viet Hoang and M{\"u}hlegg, Maximilian
and Dorn, Sebastian and others},
  journal={arXiv preprint arXiv:2004.06320},
  year={2020}
}

License

CC BY-ND 4.0

Data Summary
Type
Point Cloud, Image,
Amount
--
Size
2315.03GB
Provided by
Audi AG
The AUDI AG stands for sporty vehicles, high build quality and progressive design – for “Vorsprung durch Technik.” The Audi Group is among the world’s leading producers of premium cars.
| Amount -- | Size 2315.03GB
A2D2
2D Box 3D Semantic Segmentation 3D Box 2D Polygon 3D Instance Segmentation
Autonomous Driving
License: CC BY-ND 4.0

Overview

We have published the Audi Autonomous Driving Dataset (A2D2) to support startups and academic
researchers working on autonomous driving. Equipping a vehicle with a multimodal sensor suite,
recording a large dataset, and labelling it, is time and labour intensive. Our dataset removes
this high entry barrier and frees researchers and developers to focus on developing new technologies
instead. The dataset features 2D semantic segmentation, 3D point clouds, 3D bounding boxes, and
vehicle bus data.

Data Collection

Sensor setup

Our sensor suite consists of six cameras, five LiDAR sensors, and an automotive gateway for
recording bus data. This configuration provides 360° coverage of the environment with camera
and LiDAR. The bus data give information about vehicle state and driver control input.

Sensors

Five LiDAR sensors

  • Up to 100 m range
  • +/- 3 cm accuracy
  • 16 channels
  • 10 Hz rotation rate
  • 360° horizontal field of view
  • +/- 15° vertical field of view

Front centre camera

  • 1920 × 1208 resolution
  • 60° horizontal field of view
  • 38° vertical field of view
  • 30 fps framerate

Surround cameras (5x)

  • 1920 × 1208 resolution
  • 120° horizontal view angle
  • 73° vertical view angle
  • 30 fps framerate

Bus gateway

  • Connected to built-in car gateway
  • Connection to all car buses and their sensors
  • Timestamping and forwarding of sensor data via Ethernet

Overview of sensor carrier with sensors (top view)Overview of sensor
carrier with sensors (top view)

Other hardware

Our vehicle is equipped with additional hardware for recording
data from the sensor suite and vehicle bus. The cameras are connected to an embedded computer
via LVDS, while the LiDAR sensors are connected via a 1G-Ethernet switch. Each LiDAR sensor
is connected to a GNSS receiver which acts as a clock. A further GNSS clock serves as a time
master for the gateway and embedded computer. The bus gateway connects to the embedded computer
via 1G-Ethernet. All data is stored on a crash-safe network storage device, equipped with 48
TB of SSD storage, and accessed via 10G-Ethernet.

Overview of the recording hardware and its setupOverview
of the recording hardware and its setup

Sensor synchronization

All sensor signals are timestamped in UTC format.
Camera images are timestamped when they arrive at the embedded computer, which is synchronised
to the time master. Bus data are timestamped at the gateway, which is also synchronised to
the time master. LiDAR signals are timestamped at the sensors, which get their time from GNSS.

Calibration

LiDAR-to-Vehicle

The LiDAR sensor pose relative to the vehicle is determined by direct measurement of positions
and orientation when mounted on the vehicle.

Camera-to-Vehicle

Camera poses with respect to the vehicle are
determined by direct in-situ measurements of position and orientation.

LiDAR-to-LiDAR optimization

We use one LiDAR as a reference and initialise the other LiDAR sensor poses to their measured
positions and orientations. Next, an Iterative Closest Point algorithm is used to refine the
poses of the other LiDAR sensors within the vehicle coordinate system. This registration
uses a recording of a static environment with a static ego vehicle and does not require any
fiducial targets.

Camera-to-LiDAR optimization

The camera poses are
optimized using camera and LiDAR recordings of fiducial targets (e.g. checkerboards). Additionally
a low speed driving scene is used to improve calibration of sensor orientation. This process
uses features (e.g. edges) in camera and LiDAR data to optimize relative poses.

Data Annotation

Semantic segmentation

The dataset features 41,280 frames with semantic segmentation in 38 categories. Each pixel
in an image is given a label describing the type of object it represents, e.g. pedestrian,
car, vegetation, etc.

Point cloud segmentation

Point cloud segmentation

Point cloud segmentation is produced
by fusing semantic pixel information and LiDAR point clouds. Each 3D point is thereby assigned
an object type label. This relies on accurate camera-LiDAR registration.

3D bounding boxes

3D bounding boxes

c3D bounding boxes are provided for 12,499 frames.
LiDAR points within the field of view of the front camera are labelled with 3D bounding boxes.
We annotate 14 classes relevant to driving, e.g. cars, pedestrians, buses, etc.

3D bounding boxes

Citation

Please use the following citation when referencing the dataset:

@article{geyer2020a2d2,
  title={A2d2: Audi autonomous driving dataset},
  author={Geyer, Jakob and Kassahun, Yohannes and Mahmudi, Mentar and Ricou, Xavier and Durgesh,
Rupesh and Chung, Andrew S and Hauswald, Lorenz and Pham, Viet Hoang and M{\"u}hlegg, Maximilian
and Dorn, Sebastian and others},
  journal={arXiv preprint arXiv:2004.06320},
  year={2020}
}

License

CC BY-ND 4.0

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