Scalability in Perception for Autonomous Driving: Waymo Open Dataset

TL;DR

This paper introduces the Waymo Open Dataset, a large-scale, diverse, and high-quality dataset for autonomous driving research, enabling better generalization and benchmarking for perception tasks.

Contribution

The paper presents a new extensive dataset with 1150 scenes, diverse environments, and comprehensive annotations, addressing limitations of existing datasets in scale and variation.

Findings

15x more diverse than previous datasets

Provides strong baselines for detection and tracking

Studies effects of dataset size and geography on model performance

Abstract

The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing self-driving datasets are limited in the scale and variation of the environments they capture, even though generalization within and between operating regions is crucial to the overall viability of the technology. In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset. Our new dataset consists of 1150 scenes that each span 20 seconds, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies. It is 15x more diverse than the largest camera+LiDAR dataset available based on our proposed diversity metric. We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames. Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks. We further study the effects of dataset size and generalization across geographies on 3D detection methods. Find data, code and more up-to-date information at http://www.waymo.com/open.