Argoverse 2 (AV2) is 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 be- tween 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.

As the deployment of autonomous vehicles (AVs) becomes increasingly prevalent, ensuring safe and smooth interactions between AVs and other human agents is of critical importance. In the urban environment, how vehicles resolve conflicts has significant impacts on both driving safety and traffic efficiency. To expedite the studies on evaluating conflict resolution in AV-involved and AV-free scenarios at unsignalized intersections, this paper presents a high-quality dataset derived from the open Argoverse-2 motion forecasting data. First, scenarios of interest are selected by applying a set of heuristic rules regarding post-encroachment time (PET), minimum distance, trajectory crossing, and speed variation. Next, the quality of the raw data is carefully examined. We found that position and speed data are not consistent in Argoverse-2 data and its improper processing induced unnecessary errors. To address these specific problems, we propose and apply a data processing pipeline to correct and enhance the raw data. As a result, 5k+ AV-involved scenarios and 16k+ AV-free scenarios with smooth and consistent position, speed, acceleration, and heading direction data are obtained. Further assessments show that this dataset comprises diverse and balanced conflict resolution regimes. This informative dataset provides a valuable resource for researchers and practitioners in the field of autonomous vehicle assessment and regulation.

The Argoverse 1 open-source data collection includes:

A 3D Tracking Dataset with 113 3D annotated scenes A Motion Forecasting Dataset with 324,557 scenarios

The Argoverse 2 open-source data collection includes:

A Sensor Dataset with 1,000 3D annotated scenarios — each with lidar, ring camera, and stereo sensor data A Lidar Dataset with 20,000 unlabeled scenarios suitable for self-supervised learning A Motion Forecasting Dataset with 250,000 interesting driving scenarios with richer attributes than its predecessor, the Argoverse 1 Motion Forecasting Dataset A Map Change Dataset with 1,000 scenarios, 200 of which depict scenes that changed since mapping

Scenario Mining dataset containing referring expressions (language prompts) and 3D bounding boxes corresponding to those referring expressions. Built on the Argoverse 2 dataset.

  license: cc-by-nc-sa-4.0

language: - en tags: - Autonomous - Driving - Language pretty_name: RefAV size_categories: - 10K<n<100K