Autonomous Vehicle Data Annotation for Self-Driving AI
Self-driving systems require the most demanding annotation quality in the industry — where a single mislabeled pedestrian or misclassified road sign can mean the difference between safety and failure. Centric Labs provides the multi-modal annotation services that autonomous vehicle companies need: camera images, LiDAR point clouds, radar data, and sensor fusion — all labeled with the temporal consistency and spatial accuracy that ADAS and L4/L5 autonomy require. Our AV-specialized teams have annotated millions of frames across urban, highway, and adverse weather scenarios.
Multi-Modal Annotation for Every Sensor in the Stack
2D camera annotation including vehicle, pedestrian, cyclist, and traffic sign detection with occlusion handling. 3D LiDAR point cloud annotation with cuboid labeling, semantic segmentation, and ground plane estimation. Sensor fusion combining camera and LiDAR annotations with calibration alignment. Temporal annotation with persistent object tracking across frames and sequences. Lane and road boundary annotation for HD map creation and path planning. Scenario classification and event labeling for edge case and rare event tagging.
What you get
- Dedicated managed teams, no anonymous crowd
- Multi-stage QA with measurable SLAs
- Secure workflows designed for enterprise data
- Fast pilots with clear success criteria
Annotate Your Driving Data With AV Specialists
Send us a sample drive sequence. We will annotate across all modalities and demonstrate our temporal consistency, sensor fusion accuracy, and edge case handling.
What you get
- Dedicated managed teams, no anonymous crowd
- Multi-stage QA with measurable SLAs
- Secure workflows designed for enterprise data
- Fast pilots with clear success criteria
Ready to validate quality and security in a pilot?
We will scope a small, measurable dataset, define acceptance criteria, and stand up a managed team fast.