Autonomous driving annotation that survives the edge cases
LiDAR, multi-camera perception, scenario tagging, and rare-event labeling — delivered by domain-trained annotators with multi-layer QA, not gig-work lottery tickets.
Your CTO does not speak “generic labeling”
Autonomous stacks fail on the long tail — weather, occlusion, unusual road users, sensor dropout, and inconsistent attributes across frames.
- Label drift between day/night and weather conditions
- Broken track IDs under occlusion
- Weak scenario taxonomies for evaluation sets
- Vendors who treat AV work like retail boxes
Scenario coverage
Tag and mine hard scenes for offline evaluation and active learning loops.
Geometry fidelity
Consistent 2D/3D geometry, class maps, and attribute schemas across sensor modalities.
Edge-case discipline
Explicit handling for rare events instead of silently averaging them away.
Annotation built for the AV stack
We calibrate to your ontology, then scale managed delivery with QA checkpoints that protect model training and eval datasets.
LiDAR & 3D labeling
3D cuboids, point segmentation support, object attributes, and frame-to-frame continuity for perception models.
Camera perception
Boxes, polygons, polylines, keypoints, lanes, traffic participants, and fine-grained attributes.
Driving scenarios
Scenario tagging for cut-ins, unprotected lefts, construction zones, vulnerable road users, and more.
Edge case detection
Rare-event review, hard-negative mining support, and taxonomy-driven labeling for long-tail risk.
Multi-layer QA
Gold tasks, consensus sampling, review queues, and rework SLAs aligned to your accuracy targets.
BYOP delivery
Work in CVAT, Label Studio, Labelbox, V7, or your proprietary AV tooling — your tenancy, your controls.
Managed workforce, AV-aware process
US-registered commercial partner with an African delivery workforce trained through Mlatho Learn and scored in Mlatho Lab.
Domain training first
Annotators train on your guidelines and sample packs before production volume starts.
Pilot then scale
Short calibration batch with quality report, then expand throughput against agreed metrics.
Security-ready posture
Controlled access, NDAs, and procurement support — see mlatho.com/security.
Fast iteration
Timezone-friendly collaboration for Europe and US East Coast perception teams.
From scoping call to labeled scenes
Share samples
Ontology, guidelines, and a small representative scene pack
Calibrate
Train the team, align edge cases, lock QA gates
Pilot batch
Deliver with metrics, findings, and rework notes
Scale
Expand volume inside your preferred platform
Questions from AV teams
Ready to harden your perception datasets?
Book a short scoping call. Bring one sample set and your ontology — we will propose a calibrated pilot.
Request a Demo