# waymo-research/waymo-open-dataset

Waymo Open Dataset

Repository: https://github.com/waymo-research/waymo-open-dataset
Canonical: https://ross.abutalabs.com/products/waymo-open-dataset
Homepage: https://www.waymo.com/open
Language: Python
License: NOASSERTION
License Family: other
Topics: autonomous-driving, dataset
Last push: 2026-01-08T22:00:08+00:00

## Health v2 (maintenance only)
Score: 50/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 61, release rhythm 8, longevity 100
- inputs: {"age_days": 2638, "days_push": 237, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3398, forks 699 (observed 2026-08-28T04:08:02.618819+00:00)

## What it is
The Waymo Open Dataset is a large-scale public collection of autonomous driving datasets (Perception, Motion, and End-to-End Driving) accompanied by an Apache-licensed Python/TensorFlow codebase with dataset format definitions, evaluation metrics, and helper functions. It supports research challenges in perception, motion forecasting, sim agents, scenario generation, and vision-based end-to-end driving.

## Use cases
- train 3D object detection models on lidar point clouds
- benchmark motion forecasting models against a leaderboard
- evaluate camera segmentation and panoptic segmentation models
- build end-to-end driving models from camera data and routing commands
- generate simulated agent behavior for scenario generation research
- reconstruct 3D object shapes and NeRFs from multi-sensor data

## When to choose
- you need large-scale, high-quality labeled autonomous driving sensor data for research
- you want standardized evaluation metrics for perception or motion prediction tasks
- you are participating in Waymo Open Dataset Challenges

## When to avoid
- you need a permissively licensed dataset - the data itself has non-commercial restrictions
- you want to evaluate real-life vehicle performance or safety
- you need a lightweight dataset without large download and compute requirements

## Facets
- artifact type: dataset
- maturity: active
- function: machine-learning, computer-vision, benchmarking, data-science
- domain: autonomous-vehicles, machine-learning, computer-vision, simulation
- platform: python
- tags: autonomous-driving, lidar, point-cloud, motion-forecasting, tensorflow, evaluation-metrics, non-commercial-license, linux, gpu

## Member repositories
- waymo-research/waymo-open-dataset (main) score 50

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:02.618819+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:38:47.800589+00:00, confidence not recorded.
  - readme: https://github.com/waymo-research/waymo-open-dataset (fetched 2026-08-28T04:08:02.618819+00:00, sha 519bd4c77285)
  - homepage: https://www.waymo.com/open (fetched 2026-08-29T09:33:07.594112+00:00, sha 6503f7a650a4)
  - site_page: https://www.waymo.com/open/about (fetched 2026-08-29T09:33:07.602968+00:00, sha 207a2346ec53)
  - site_page: https://www.waymo.com/open/faq (fetched 2026-08-29T09:33:07.605599+00:00, sha 51ebd1411445)
  - site_page: https://www.waymo.com/faq (fetched 2026-08-29T09:33:07.608098+00:00, sha 6c1eb0fd52ca)
- Data as of 2026-08-30T08:39:29.467469+00:00.