Sensors must agree
A cuboid that is right in LiDAR and wrong in the camera breaks fusion in ways that are hard to trace.
ADAS, autonomy and driver monitoring
Training Data for Automotive and Mobility AI
Perception stacks are only as good as the frames behind them. We deliver multi-sensor annotation with the geometric precision autonomy programmes are audited against.
Perception That Holds Up on the Road
An autonomy programme spends most of its data budget on the long tail: the cut-in, the occluded pedestrian, the roadworks that look like nothing in the training set. We label for that tail deliberately, across camera, LiDAR and radar, with the frame-to-frame consistency that tracking and fusion depend on.
Where Automotive & Mobility AI Programmes Struggle
Temporal consistency
Object identity has to hold across frames or every tracking metric becomes noise.
The tail is the product
Common scenes are cheap and nearly useless. The rare ones decide whether the system ships.
Safety work is audited
Annotation decisions have to be explainable months later, to people who were not in the room.
AI Use Cases We Support
2D and 3D object annotation
Boxes and cuboids for vehicles, pedestrians, cyclists and static obstacles.
Lane and drivable space
Lane geometry, boundaries and free-space segmentation.
Traffic sign and signal
Classification including regional variants, damage and partial occlusion.
Driver and cabin monitoring
Gaze, drowsiness and distraction labelling from in-cabin capture.
Sensor fusion sets
Time-synchronised camera, LiDAR and radar annotation.
Scenario mining
Finding and labelling the edge cases already sitting in your fleet logs.
Data Types We Handle
- Multi-camera video Surround capture with consistent identity across views.
- LiDAR point clouds Cuboid and semantic segmentation with calibration checks.
- Radar returns Association and annotation alongside vision.
- Vehicle telemetry CAN and IMU series aligned to the perception frames.
How We Run the Programme
- Calibration verified first Nothing is annotated until extrinsics are checked. Bad calibration produces confidently wrong labels.
- Track-level review Sequences are reviewed as sequences, not as independent frames.
- Tail-first sampling We plan the rare-event coverage before the volume.
- Full audit trail Who labelled, who reviewed, and what the guideline said that day.
Build Automotive & Mobility AI on Data You Can Trust
Tell us what you are building and we will come back with a plan, a sample and a price.