List of Datasets

Automotive

rounD Dataset

Road-user trajectory prediction and interaction at roundabouts

Naturalistic 25 Hz trajectories for 13,746 road users across 24 recordings and more than six hours at three German roundabouts.

Metadata: Complete Metadata source documented Released 2020

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

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

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 + heading Other Not reported Not reported 25 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
Accuracy and notes
Accuracy notes
No quantitative trajectory accuracy is reported.
Reference notes
DeepLab-v3+ detections were tracked, RTS-smoothed, sometimes interpolated through occlusion, transformed to a local coordinate system, and manually checked.

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

Lens calibration and first-frame stabilization were applied during processing; released calibration parameters or source calibration material are not established.

Known or intentional limitations

Reported constraints and characteristics to check before using the dataset.

Recordings were restricted to good weather, adequate lighting, and little wind.

Vulnerable road users were infrequent, and no collisions were recorded.

Temporary occlusions may be filled by trajectory interpolation.

Cameras

Not reported

IMUs

Not reported

GNSS

Not reported

LiDAR

Not reported

Additional sensors

Not reported

Annotations

Reported annotation availability and task support.

Availability
Available
Format
CSV recording metadata, track metadata, and per-frame trajectories with class, position, heading, dimensions, velocity, and acceleration
Class count
[car van truck bus trailer pedestrian bicycle motorcycle]
Semantic segmentation Not reported
Instance segmentation Not reported
Object detection Available
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
rounDdataset

BibTeX

@inproceedings{rounDdataset,
  title={The rounD Dataset: A Drone Dataset of Road User Trajectories at Roundabouts in Germany},
  author={Krajewski, Robert and Moers, Tobias and Bock, Julian and Vater, Lennart and Eckstein, Lutz},
  booktitle={2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)},
  pages={1--6},
  year={2020},
  doi={10.1109/ITSC45102.2020.9294728}
}

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