Perception
Prepare camera and point-cloud ground truth for object detection, segmentation, and tracking model development.
Independent infrastructure for automotive
Turn every drive into better model data.
Avala connects synchronized camera, LiDAR, radar, and IMU data with curation, human and AI review, quality control, and versioned releases—so autonomy teams can move from road event to training input with lineage intact.
One operating record from source session to dataset release.
Autonomy dataset pipeline
Streams alignedScenario slice
urban-night / construction- Camera
- LiDAR
- Radar
- IMU & telemetry
Timestamps and source lineage preserved
- 01
Ingest synchronized sessions
- 02
Curate the scenarios that matter
- 03
Build defensible ground truth
- 04
Publish a versioned release
Camera
LiDAR
Radar
IMU & telemetry
One operating loop
The road event is only the beginning.
Replace fragmented handoffs with a traceable path from multimodal capture to the next dataset release.
- 01
Ingest synchronized sessions
Bring MCAP, video, point clouds, and telemetry into synchronized sessions without stripping away source context.
- 02
Curate the scenarios that matter
Search and slice sessions for the edge cases, environments, and behaviors that belong in the next training cycle.
- 03
Build defensible ground truth
Combine model assistance with human 2D, 3D, and 4D review, then apply the quality gates each release requires.
- 04
Publish a versioned release
Freeze the manifest contents and source origins, attach declared rights metadata, then publish an immutable revision that the loader resolves for downstream training and evaluation.
Durable dataset identity
A release your autonomy stack can reproduce.
Treat every training set as a governed artifact, not a one-off folder. Avala publishes an immutable manifest, a resolvable revision, source origins, and declared rights metadata.
- Friendly dataset reference plus an immutable canonical revision
- Manifest digest, object count, total size, and source origins
- Declared rights document and access terms attached to the revision
- Published state and revision identity returned through the loader
Dataset release manifest
Versioned- Dataset
- autonomy/urban-night
- Release
- v24
- Origin records
- primary / avala · calibration / avala
- Declared rights
- Attached
- Revision state
- Published
autonomy/urban-night@v24Built for automotive AI
One data system across the autonomy program.
Keep perception, sensor-fusion, in-cabin, and mapping work on the same dataset identity and release discipline.
Sensor fusion
Preserve timestamp and calibration context while teams inspect the same event across every sensor stream.
In-cabin intelligence
Organize visual and telemetry data for occupant- and driver-state model development with explicit review criteria.
Mapping & localization
Curate spatial data for mapping, localization, road understanding, and change-detection workflows.
Bring a real program
Map your hardest automotive data loop.
Bring one data flow, one release gate, and the stack it needs to connect to. We’ll show how Avala can operationalize it without breaking lineage.