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Lyft-prediction
3D Box Tracking
Autonomous Driving
|...
License: CC BY-NC-SA 4.0

Overview

The largest collection of traffic agent motion data.

This dataset includes the logs of movement of cars, cyclists, pedestrians, and other traffic
agents encountered by our autonomous fleet. These logs come from processing raw lidar, camera,
and radar data through our team’s perception systems and are ideal for training motion prediction
models. The dataset includes:

  • hours of traffic agent movement.(1000+ )
  • miles of data from 23 vehicles.(16K)
  • semantic map annotations.(15K)

Data Collection

Level 5’s in-house sensor suite

Lyft

Lidar

Our vehicles are equipped with 40 and 64-beam lidars on the roof and
bumper. They have an Azimuth resolution of 0.2 degrees and jointly produce ~216,000 points
at 10 Hz. Firing directions of all lidars are synchronized.

Lyft

Camera

Our vehicles are also equipped with six 360° cameras built
in-house. One long-focal camera points upward. Cameras are synchronized with the lidar so the
beam is at the center of each camera’s field of view when images are captured.

Data Format

The dataset is provided in zarr format. The zarr files are flat, compact, and highly performant
for loading. To read the dataset please use our new Python software kit.

The dataset consists of frames and agent states. A frame is a snapshot in time which consists
of ego pose, time, and multiple agent states. Each agent state describes the position, orientation,
bounds, and type.

 FRAME_DTYPE = [
  (“timestamp”, np.int64), // Time the frame occurred.
  (“agent_index_interval”, np.int64, (2,)), // Agents contained within the scene.
  (“ego_translation”, np.float32, (3,)), // Position of ego vehicle, used
for image generation and pose transformations.
  (“ego_rotation”, np.float32, (3, 3)), //
Rotation of ego vehicle, used for image generation and pose transformations.
 ]

 AGENT_DTYPE = [
  (“centroid”, np.float32, (2,)), // Center of the agent.
  (“extent”, np.float32, (3,)), // Size of agent.
  (“yaw”, np.float32), // Agent heading.
  (“velocity”, np.float32, (2,)), // Agent velocity (relative to heading, x is forward, y is left)
  (“track_id”, np.int32), // Unique identifier for agent
  (“label_probabilities”,
np.float32, (len(LABELS),)), // Probability of the agent being a car, truck, pedestrian, etc.
 ]

Citation

Please use the following citation when referencing the dataset:

@misc{lyft2020,
title = {One Thousand and One Hours: Self-driving Motion Prediction Dataset},
author = {Houston, J. and Zuidhof, G. and Bergamini, L. and Ye, Y. and Jain, A. and Omari,
S. and Iglovikov, V. and Ondruska, P.},
year = {2020},
howpublished = {\url{https://level5.lyft.com/dataset/}}
}

License

CC BY-NC-SA 4.0

Data Summary
Type
Image,
Amount
--
Size
71.81GB
Provided by
Lyft Level 5
Lyft’s mission is to improve people’s lives with the world’s best transportation. From rideshare to bikes and scooters, we’re constantly innovating new ways to get around. With self-driving, we can increase safety and access for people wherever they are.
Annotated by
Scale AI, Inc
Trusted by world class companies, Scale delivers high quality training data for AI applications such as self-driving cars, mapping, AR/VR, robotics, and more
| Amount -- | Size 71.81GB
Lyft-prediction
3D Box Tracking
Autonomous Driving
License: CC BY-NC-SA 4.0

Overview

The largest collection of traffic agent motion data.

This dataset includes the logs of movement of cars, cyclists, pedestrians, and other traffic
agents encountered by our autonomous fleet. These logs come from processing raw lidar, camera,
and radar data through our team’s perception systems and are ideal for training motion prediction
models. The dataset includes:

  • hours of traffic agent movement.(1000+ )
  • miles of data from 23 vehicles.(16K)
  • semantic map annotations.(15K)

Data Collection

Level 5’s in-house sensor suite

Lyft

Lidar

Our vehicles are equipped with 40 and 64-beam lidars on the roof and
bumper. They have an Azimuth resolution of 0.2 degrees and jointly produce ~216,000 points
at 10 Hz. Firing directions of all lidars are synchronized.

Lyft

Camera

Our vehicles are also equipped with six 360° cameras built
in-house. One long-focal camera points upward. Cameras are synchronized with the lidar so the
beam is at the center of each camera’s field of view when images are captured.

Data Format

The dataset is provided in zarr format. The zarr files are flat, compact, and highly performant
for loading. To read the dataset please use our new Python software kit.

The dataset consists of frames and agent states. A frame is a snapshot in time which consists
of ego pose, time, and multiple agent states. Each agent state describes the position, orientation,
bounds, and type.

 FRAME_DTYPE = [
  (“timestamp”, np.int64), // Time the frame occurred.
  (“agent_index_interval”, np.int64, (2,)), // Agents contained within the scene.
  (“ego_translation”, np.float32, (3,)), // Position of ego vehicle, used
for image generation and pose transformations.
  (“ego_rotation”, np.float32, (3, 3)), //
Rotation of ego vehicle, used for image generation and pose transformations.
 ]

 AGENT_DTYPE = [
  (“centroid”, np.float32, (2,)), // Center of the agent.
  (“extent”, np.float32, (3,)), // Size of agent.
  (“yaw”, np.float32), // Agent heading.
  (“velocity”, np.float32, (2,)), // Agent velocity (relative to heading, x is forward, y is left)
  (“track_id”, np.int32), // Unique identifier for agent
  (“label_probabilities”,
np.float32, (len(LABELS),)), // Probability of the agent being a car, truck, pedestrian, etc.
 ]

Citation

Please use the following citation when referencing the dataset:

@misc{lyft2020,
title = {One Thousand and One Hours: Self-driving Motion Prediction Dataset},
author = {Houston, J. and Zuidhof, G. and Bergamini, L. and Ye, Y. and Jain, A. and Omari,
S. and Iglovikov, V. and Ondruska, P.},
year = {2020},
howpublished = {\url{https://level5.lyft.com/dataset/}}
}

License

CC BY-NC-SA 4.0

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