List of Datasets

Automotive

Argoverse Motion Forecasting

Map-aware vehicle motion forecasting

Mined five-second 2D agent/context trajectories with city lane, ground-height and driveable-area maps.

Metadata: Complete Metadata curator reviewed Released 2019

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

Supplementary

Not reported

Quick facts

Vehicle type
Automotive
Environment
Outdoor
Data origin
Real-world
Sensor count
0
Ground truth
Available
Calibration
Not reported
Annotations
Available

Sensor overview

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

Cameras

Not reported

IMUs

Not reported

GNSS

Not reported

LiDAR

Not 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.
2D Other Not reported Not reported 10 Hz Full 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
Automatically mined tracked centroids are the benchmark reference; they are noisy estimated trajectories, not exhaustive human-verified or independent survey measurements.

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.

Mined trajectories contain noise and are not exhaustively manually verified.

Selected behavior-rich snippets are biased toward turns, intersections and lane changes.

Consecutive snippets overlap by 2.5 seconds; snippet durations are not unique recording coverage.

This entry describes Argoverse 1, separate from Argoverse 2. Paper-era scope is retained; later annotation/derived-product inventories require separate review.

Official AV1 overview repeats the paper count of 324557 snippets, while another block says 323557. The paper/overview count is retained pending manifest review; overlapping snippets are not unique recording duration.

Cameras

Not reported

IMUs

Not reported

GNSS

Not reported

LiDAR

Not reported

Radar

Not reported

Additional sensors

Not reported

Annotations

Reported annotation availability and task support.

Availability
Available
Format
2D city-frame tracked centroids with focal-agent and context roles; exact serialization is not stated here.
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
chang2019argoverse

BibTeX

@misc{chang2019argoverse,
  title = {Argoverse: 3D Tracking and Forecasting with Rich Maps},
  author = {Ming-Fang Chang and John Lambert and Patsorn Sangkloy and Jagjeet Singh and Sławomir Bąk and Andrew Hartnett and De Wang and Peter Carr and Simon Lucey and Deva Ramanan and James Hays},
  year = {2019},
  eprint = {1911.02620v1},
  archivePrefix = {arXiv}
}

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