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The research community has increasing interest in autonomous drivingresearch, despite the resource intensity of obtaining representative real worlddata. Existing self-driving datasets are limited in the scale and variation ofthe environments they capture, even though generalization within and betweenoperating regions is crucial to the overall viability of the technology. In aneffort to help align the research community's contributions with real-worldself-driving problems, we introduce a new large scale, high quality, diversedataset. Our new dataset consists of 1150 scenes that each span 20 seconds,consisting of well synchronized and calibrated high quality LiDAR and cameradata captured across a range of urban and suburban geographies. It is 15x morediverse than the largest camera+LiDAR dataset available based on our proposeddiversity 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 andtracking tasks. We further study the effects of dataset size and generalizationacross geographies on 3D detection methods. Find data, code and more up-to-dateinformation at http://www.waymo.com/open.

Scalability in Perception for Autonomous Driving: Waymo Open Dataset