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Argoverse 2.0: Next Generation Datasets for Self-driving Perception and Forecasting
Benjamin Wilson · William Qi · Tanmay Agarwal · John Lambert · Jagjeet Singh · Siddhesh Khandelwal · Bowen Pan · Ratnesh Kumar · Andrew Hartnett · Jhony Kaesemodel Pontes · Deva Ramanan · Peter Carr · James Hays

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in Dataset and Benchmark Poster Session 2 »

We introduce Argoverse 2.0 -- a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 25 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 100,000 scenarios mined for interesting and challenging interactions between the AV and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

Author Information

Benjamin Wilson (Georgia Tech)
William Qi (Argo AI)
Tanmay Agarwal
John Lambert (Georgia Tech, Argo AI)
Jagjeet Singh (Argo AI)
Siddhesh Khandelwal (University of British Columbia)
Bowen Pan (Massachusetts Institute of Technology)
Ratnesh Kumar (INRIA)
Andrew Hartnett (Argo AI)
Jhony Kaesemodel Pontes (Queensland University of Technology)
Deva Ramanan (Carnegie Mellon University)
Peter Carr (Australian National University)
James Hays (Argo AI / Georgia Tech)

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