TITLE: Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting 

ABSTRACT: We introduce Argoverse 2 (AV2) - 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 26 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
250,000 scenarios mined for interesting and challenging interactions between
the autonomous vehicle 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.
We introduce Argoverse 2 (AV2) - 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 26 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
250,000 scenarios mined for interesting and challenging interactions between
the autonomous vehicle 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.
Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
http://arxiv.org/abs/2301.00493v1