waymo-open-dataset
A collection of autonomous driving datasets and evaluation code for advancing machine perception and self-driving research.
What is waymo-open-dataset?
Waymo Open Dataset is a collection of autonomous driving datasets and evaluation tools released by Waymo to advance research in machine perception and self-driving technology. It includes high-resolution sensor data from real-world driving scenarios across three specialized datasets: Perception, Motion, and End-To-End Driving. The project provides standardized evaluation metrics and helper functions to help researchers develop and benchmark models for object detection, motion forecasting, and driving behavior prediction.
Researchers and academics working on autonomous driving, computer vision, and machine learning who need large-scale, real-world sensor data for training and evaluating perception and motion forecasting models.
Researchers choose Waymo Open Dataset because it offers one of the largest and most diverse publicly available autonomous driving datasets, collected by industry-leading autonomous vehicles under varied real-world conditions. The inclusion of official evaluation metrics ensures standardized benchmarking, while the comprehensive sensor data (LiDAR, camera, radar) enables multi-modal research that closely mirrors actual autonomous driving challenges.
Overview
Waymo Open Dataset
Use Cases
Best For
- Training object detection and tracking models for autonomous vehicles
- Researching motion forecasting and trajectory prediction algorithms
- Developing end-to-end driving models using real-world sensor data
- Benchmarking perception algorithms against standardized metrics
- Studying multi-sensor fusion techniques (LiDAR, camera, radar)
- Academic research in autonomous driving and computer vision
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