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

TL;DR

Argoverse 2 introduces three comprehensive datasets for self-driving perception and forecasting, including multimodal sensor data, large-scale lidar sequences, and challenging motion scenarios, to advance autonomous vehicle research.

Contribution

The paper presents the largest collection of multimodal perception, lidar, and motion forecasting datasets, enabling new research avenues in self-driving technology.

Findings

Largest lidar dataset supporting self-supervised learning

Extensive multimodal data with high-resolution imagery and lidar

Rich motion forecasting scenarios for complex interactions

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.