List of Datasets

Automotive

TUMTraf V2X

Cooperative 3D perception and tracking

Vehicle/roadside camera-lidar data at an urban intersection with tracked 3D boxes and challenging occlusions.

Metadata: Complete Metadata source documented Released 2024

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
7
Ground truth
Available
Calibration
Parameters Only
Annotations
Available

Sensor overview

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

Cameras

5 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.
Other Manual annotation 3D BAT v24.3.2 Not reported Not reported 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
Manually reviewed oriented 3D boxes with persistent track IDs on selected labeled scenes; annotations derive from registered lidar/camera observations.

Calibration and synchronization

Reported calibration level, reproducibility signals, and supporting notes.

Level
Parameters Only
Processed parameters
Available
Raw calibration data
Not reported
Calibration targets
Not reported

Parameters only

Processed calibration parameters are reported, but raw calibration data is not reported.

Notes

Official devkit provides calibration loaders and intrinsic/extrinsic projection products. Complete raw sessions and verified independent inter-platform alignment error remain unknown.

Known or intentional limitations

Reported constraints and characteristics to check before using the dataset.

Eight ten-second scenes include seven daytime and one nighttime run. Images, point clouds and object boxes use different denominators; conflicting approximately 30k, 29.38k and 24,777 box totals remain unasserted.

Four fixed roadside cameras/one LiDAR and one vehicle camera/one LiDAR have different viewpoints. Paper GPS/IMU registration hardware is not treated as independently downloadable native input without a matching manifest.

NTP/nearest-time pairing has a reported 24.91 ms mean offset, not a worst-case or hardware-synchronization guarantee. Offline evaluation omits communication latency and does not cover all weather scenarios.

Tracked object boxes derive from registered images/clouds with manual review; they are not an independent platform-pose trajectory. Calibration parameters do not establish raw sessions or uniform geometric label accuracy.

Cameras

Camera 1

Model
Basler ace acA1920-50gc
Setup
Mono
Count
4
Effective cameras
4
Modality
Rgb
Resolution
1920 x 1200
Rate
15 Hz
Shutter
Not Reported
Lens type
Not Reported
HFOV
Not reported
VFOV
Not reported
Notes
Roadside cameras; sensor placement is preserved in notes.

Camera 2

Model
Basler ace acA1920-50gc
Setup
Mono
Count
1
Effective cameras
1
Modality
Rgb
Resolution
1920 x 1200
Rate
15 Hz
Shutter
Not Reported
Lens type
Not Reported
HFOV
Not reported
VFOV
Not reported
Notes
Vehicle camera.

IMUs

Not reported

GNSS

Not reported

LiDAR

Ouster OS1-64 gen.2

Model or name
Ouster OS1-64 gen.2
Count
1
Dimensions
3
Rate
10 Hz
Channels
64
Range
Not reported

Robosense RS-LiDAR-32

Model or name
Robosense RS-LiDAR-32
Count
1
Dimensions
3
Rate
10 Hz
Channels
32
Range
Not reported

Radar

Not reported

Additional sensors

Not reported

Annotations

Reported annotation availability and task support.

Availability
Available
Format
ASAM OpenLABEL oriented 3D boxes, eight object classes, track IDs and contextual attributes.
Class count
8
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
zimmer2024tumtraf

BibTeX

@article{zimmer2024tumtraf,
    title={TUMTraf V2X Cooperative Perception Dataset},
    author={Zimmer, Walter and Wardana, Gerhard Arya and Sritharan, Suren and Zhou, Xingcheng and Song, Rui and Knoll, Alois},
    journal={arXiv preprint arXiv:2403.01316},
    year={2024}
}

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