List of Datasets

Automotive

Argoverse 2 LiDAR

Self-supervised LiDAR learning and point-cloud forecasting

Twenty thousand thirty-second LiDAR scenarios with map-aligned ego poses and local vector maps, without imagery or object-box annotations.

Metadata: Complete Metadata source documented Released 2022

Primary places to inspect, download, read about, or reproduce the dataset.

Quick facts

Vehicle type
Automotive
Environment
Outdoor
Data origin
Real-world
Sensor count
2
Ground truth
Available
Calibration
Not reported
Annotations
Not available

Sensor overview

High-level sensor availability before the detailed sensor records below.

Cameras

Not reported

IMUs

Not reported

GNSS

Not reported

LiDAR

2 reported

Radar

Not reported

Magnetometers

Not reported

Barometers

Not reported

UWB

Not reported

Ground truth

Reference scope, method, rate, coverage, and provenance.

Scope Method Reference system Count Rate Coverage Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
6DoF Other Map-aligned ego pose Not reported Not reported Unknown Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
Derived
Reference notes
Official AV2 page states combined GPS-/sensor-based 6-DoF localization. The map-aligned estimate depends on recorded sensing; detailed fusion stages, valid intervals and independent errors remain unknown.

Calibration and synchronization

Reported calibration level, reproducibility signals, and supporting notes.

Level
Not reported
Processed parameters
Not reported
Raw calibration data
Not reported
Calibration targets
Not reported

Calibration data

Calibration reproducibility details are not reported.

Notes

Not reported

Known or intentional limitations

Reported constraints and characteristics to check before using the dataset.

Twenty thousand thirty-second scenarios provide LiDAR, ego poses and maps. No imagery or 3D object annotations are included; collection-rig cameras do not establish a normal input for this product.

Returns from two out-of-phase 32-beam scanners are aggregated and egomotion-compensated. A 64-beam combined sweep is not a single physical scanner, and frame quantization differs from per-point timestamps.

Ego poses combine GPS-/sensor-based localization. Raw navigation observations, exact fusion details and independently measured trajectory accuracy are not established; map alignment is not author confirmation.

Nominal clip duration sums can include curation/overlap and do not prove unique fleet driving time. Forecasting split semantics differ from the AV2 Sensor and Motion Forecasting products.

Cameras

Not reported

IMUs

Not reported

GNSS

Not reported

LiDAR

Velodyne VLP-32C

Model or name
Velodyne VLP-32C
Count
2
Dimensions
3
Rate
10 Hz
Channels
32
Range
Not reported

Radar

Not reported

Additional sensors

Not reported

Automotive

Argoverse 2 Motion Forecasting

Two hundred fifty thousand eleven-second urban scenarios with planar actor histories, futures, headings and vector maps; no raw sensor streams.

Automotive

Argoverse 2 Sensor

One thousand fifteen-second driving scenarios with nine cameras, paired LiDAR sweeps, tracked 3D boxes and scenario-local HD maps.

Annotations

Reported annotation availability and task support.

Availability
Not available
Format
Not reported
Class count
Not reported
Semantic segmentation Not reported
Instance segmentation Not reported
Object detection Not reported
Optical flow Not reported
Depth ground truth Not reported

Citation

Citation key and BibTeX kept at the end of the page for reference.

Citation key
wilson2021argoverse2

BibTeX

@INPROCEEDINGS { wilson2021argoverse2,
  author = {Benjamin Wilson and William Qi and Tanmay Agarwal and John Lambert and Jagjeet Singh and Siddhesh Khandelwal and Bowen Pan and Ratnesh Kumar and Andrew Hartnett and Jhony Kaesemodel Pontes and Deva Ramanan and Peter Carr and James Hays},
  title = {Argoverse 2: Next Generation Datasets for Self-driving Perception and Forecasting},
  booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021)},
  year = {2021}
}

Dataset corrections

Notice a specific issue after reviewing the page?

Report an inaccuracy