About

What's included

Here's what's currently included in our unique Waymo Open Dataset. We plan to continue growing this dataset, so make sure to continue visiting the page for our most recent updates.

Perception Dataset (last updated March 2024)

• [New: March 2024: v. 1.4.3 and v. 2.0.1] We made small improvements in the 3D semantic segmentation ground truth labels, especially for the class of motorcyclist.

• 2,030 segments of 20s each, collected at 10Hz (390,000 frames) in diverse geographies and conditions

• Perception object assets data in a modular format (v2.0.0)

  • Extracted perception objects from multi-sensor data: all 5 cameras and the top lidar

  • Lidar features include 3D point cloud sequences that support 3D object shape reconstruction. We additionally provide refined box pose through point cloud shape registration for all objects in the vehicle category

  • Camera features include sequences of camera patches from the most_visible_camera, projected lidar returns on the corresponding camera, per-pixel camera rays information, and auto-labeled 2D panoptic segmentation that supports object NeRF reconstruction, detailed in the paper

  • 1.2M object frames from 31K unique object instances that covers 2 classes (vehicles and pedestrians

  • Check out our related paper, Scalability in Perception for Autonomous Driving: Waymo Open Dataset, for additional details on scale, diversity, and object detection and tracking baselines of the original release.

  • Sensor data

    • 1 mid-range lidar

    • 4 short-range lidars

    • 5 cameras (front and sides)

    • Synchronized lidar and camera data

    • Lidar to camera projections

    • Sensor calibrations and vehicle poses

  • Bounding box labels

    • Labels for 4 object classes - Vehicles, Pedestrians, Cyclists, Signs

    • High-quality labels for lidar data in 1,200 segments

    • 12.6M 3D bounding box labels with tracking IDs on lidar data

    • High-quality labels for camera data in 1,000 segments

    • 11.8M 2D bounding box labels with tracking IDs on camera data

  • 2D video panoptic segmentation labels

    • For a subset of 100k camera images

    • Labels for 28 classes

    • Including: Car, Bus, Truck, Other Large Vehicle, Trailer, Ego Vehicle, Motorcycle, Bicycle, Pedestrian, Cyclist, Motorcyclist, Ground Animal, Bird, Pole, Sign, Traffic Light, Construction Cone, Pedestrian Object, Building, Road, Sidewalk, Road Marker, Lane Marker, Vegetation, Sky, Ground, Static, Dynamic

    • Instance segmentation labels are provided for the Vehicle, Pedestrian and Cyclist classes and are consistent both across cameras and over time

  • Key point labels

    • Labels for 2 object classes - Pedestrians and Cyclists

    • 14 key points from nose to ankle

    • 200k object frames with 2D key point labels

    • 10k object frames with 3D key point labels

  • 3D semantic segmentation labels

    • Segmentation labels for 1,150 segments

    • Labels for 23 classes

    • Including: Car, Truck, Bus, Motorcyclist, Bicyclist, Pedestrian, Sign, Traffic Light, Pole, Construction Cone, Bicycle, Motorcycle, Building, Vegetation, Tree Trunk, Curb, Road, Lane Marker, Walkable, Sidewalk, Other Ground, Other Vehicle, Undefined

  • Map data

    • 3D road graph data for each segment

    • Includes lane centers, lane boundaries, road boundaries, crosswalks, speed bumps, stop signs, and entrances to driveways

  • Association of 2D and 3D bounding boxes

    • Corresponding object IDs are provided for 2 object classes - Pedestrians and Cyclists

  • Data for 3D Camera-Only Detection Challenge, with 80 segments of 20s camera imagery

• Code

  • Access the GitHub repo here

• Check out our related paper, Scalability in Perception for Autonomous Driving: Waymo Open Dataset, for additional details on scale, diversity, and object detection and tracking baselines of the original release

Motion Dataset (last updated March 2024)

• Sensor data

  • 1 mid-range lidar

  • 4 short-range lidars

  • [New] 8 cameras with 360 degree coverage — image embeddings only

• [New: March 2024: v 1.2.1 dataset corrections]

  • The initial release of the WOMD camera data contained misalignment between LiDAR data and roadgraph inputs for some frames. The 1.2.1 release provides new timestamps for the lidar data with an updated pose transformation matrix per time step.

• 103,354, 20s 10Hz segments (over 20 million frames), mined for interesting interactions

• 574 hours of data

• Sensor data

  • 1 mid-range lidar

  • 4 short-range lidars

• Object data

  • 10.8M objects with tracking IDs

  • Labels for 3 object classes - Vehicles, Pedestrians, Cyclists

  • 3D bounding boxes for each object

  • Mined for interesting behaviors and scenarios for behavior prediction research, such as unprotected turns, merges, lane changes, and intersections

  • 3D bounding boxes are generated by a model trained on the Perception Dataset and detailed in our paper: Offboard 3D Object Detection from Point Cloud Sequences

• Map data

  • 3D map data for each segment

  • Locations include: San Francisco, Phoenix, Mountain View, Los Angeles, Detroit, and Seattle

  • Added entrances to driveways (the map already Includes lane centers, lane boundaries, road boundaries, crosswalks, speed bumps and stop signs)

  • Adjusted some road edge boundary height estimates

• Code

  • Access the GitHub repo here

• Check out our related paper, Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset, for additional details on the dataset and baseline models.

End-to-End Driving Dataset (last updated March 2025)

• Sensor data

  • Images from 8 cameras (front, side, and back) providing 360 degree coverage

  • 5,000 segments of 20s each in diverse locations across multiple cities

• Planning Data

  • Past 4-second trajectory at 4Hz frequency

  • ADV's velocity and acceleration, aligned with the past trajectory

  • (Training set only) ADV's future 5-second trajectory, in the same format and frequency as the past trajectory

• Route data

  • Text-based routing instructions

• Code

  • Access the GitHub repo here

The Waymo Open Dataset is licensed for non-commercial use. You can find the license agreement here. While this dataset is not reflective of the full capabilities of our systems, and is only a fraction of the data on which Waymo’s autonomous driving system is trained, we believe that for research purposes this large, diverse, and high-quality dataset should be extremely valuable.

Diversity of Geographics

Waymo Open Dataset is collected from a wide variety of places.