Multi-camera, multi-sensor annotation for perception, prediction, and planning models — combining image, video, and LiDAR labeling into a single consistent pipeline.
Bounding box and segmentation labeling across synchronized front, side, and rear camera feeds.
Full 3D object labeling fused with camera frames — see our LiDAR annotation service.
Lane lines, drivable area, signage, and traffic-signal labeling for planning models.
Tracked object paths across frames for motion-prediction and behavior models.
Autonomous driving datasets run through the same three-layer QA as everything else we label — annotator, reviewer, senior sign-off — with edge-case libraries built specifically around your operational design domain.
Yes — camera, LiDAR, and radar frames are labeled as a synchronized scene, not in isolation, so object IDs stay consistent across sensors.
Yes. We build an edge-case library with your team during the calibration phase so rare scenarios — occlusion, adverse weather, unusual objects — are labeled consistently.
Every asset passes annotator, reviewer, and senior QA review, with disagreement rates and accuracy metrics tracked and shared throughout the project.
Share your requirements to discuss a suitable pilot and proposal scope.