mlsanityData to 7 Oct 2026

Scalability in Perception for Autonomous Driving: Waymo Open Dataset

arXiv 1912.04838 ↗First posted 10 Dec 2019cs.CV cs.LG stat.MLcited by 2,436

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Cited by 237 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 2,436 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Dec 2019.

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Citing papers per year: 2020: 2; 2021: 180; 2022: 308; 2023: 423; 2024: 494; 2025: 591; 2026: 438.

2,436 papers.

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  18. 2024Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllabilitycited by 337
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  30. 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learningcited by 281
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