A quick look at how Graveiens AI partners with teams to deliver human data for AI models.
From driver monitoring to road perception, we build and label the data ADAS and AV stacks rely on.
Autonomous vehicle data annotation is how ADAS and self-driving stacks learn to read the road: labeling pedestrians, vehicles, lanes, signs and traffic lights across camera, LiDAR and radar, plus in-cabin driver-monitoring data. Graveiens AI delivers autonomous vehicle data annotation to automotive-grade standards — trained annotators and SMEs work to your ontology, and a four-stage QA workflow with gold-set benchmarking keeps every frame accurate and audit-ready. We combine it with LiDAR data annotation, image annotation and scenario data collection, so one partner can cover perception data end to end, from capture to model-ready labels.
Start an ADAS pilotAdvanced driver-assistance systems must perceive the road accurately in every condition. Graveiens AI collects and annotates the multi-sensor data that ADAS models depend on — camera, LiDAR and radar — with precise 2D and 3D labels reviewed against a strict accuracy bar.
We adapt tooling and guidelines to your ontology, from lane and sign detection to 3D cuboids and sensor fusion, and hold every batch to a measured quality standard through our four-stage review.
Talk to our ADAS data teamPerception, fusion and event data for assisted driving.
2D annotation of vehicles, pedestrians, signs and lane markings for perception models.
LiDAR cuboids and fused camera-LiDAR-radar labelling for robust perception.
Gaze, drowsiness and occupant labelling for cabin-safety features.
Rare-scenario tagging to strengthen safety-critical behaviour.
Representative programmes our ADAS data services support.
Detection data for braking and warning systems.
Lane and boundary data for steering assistance.
In-cabin data for attention and drowsiness models.
In ADAS, label quality is a safety matter. We combine trained annotators, expert review and gold-standard checks on every batch, handle consent and privacy for in-cabin data, and invoice only for approved deliverables — so you can validate quality before scaling.
Start an ADAS pilotHow our autonomous vehicle data annotation compares with other ADAS and AV providers on 3D coverage and QA.
| Provider | Core focus | Modalities | QA / accuracy approach | Engagement model |
|---|---|---|---|---|
| Graveiens AIUs | Autonomous vehicle data annotation — 3D, in-cabin, driver monitoring | Image, video, 3D/LiDAR, in-cabin | Automotive-grade QA vs a gold standard, ISO 9001:2017 | Pay-on-approval pilots, managed programs |
| Macgence | ADAS and autonomous driving data annotation | Image, video, 3D/LiDAR | Managed crowd with human QA | Project-based managed teams |
| Cogito Tech | ADAS and 3D perception annotation | Image, video, 3D | Human-in-the-loop QA | Managed teams |
| Shaip | In-cabin and driver-monitoring data | Image, video, audio | Domain-expert QA | Off-the-shelf datasets plus services |
| iMerit | ADAS and 3D/LiDAR annotation | Image, video, 3D/LiDAR | Expert-in-the-loop QA | Dedicated managed teams |
| Sama | ADAS sensor and image annotation | Image, video, 3D/LiDAR | SamaAssure QA | Managed workforce |
| Surge AI | Multimodal data for LLMs | Text, image, dialogue | Expert human raters | API plus managed service |
Compliance-first delivery and a pay-on-approval model that de-risks every engagement.
Compliant handling of in-cabin and road data.
Consistency across frames and sensors.
Collection, annotation and validation in one place.
Invoiced only for approved deliverables.
Send us a sample task. You only pay for deliverables you approve.
Book a pilot