Sharper motion tracking and scene understanding for safer autonomous trucks.
Read the story- faster labeling workflows
- 30%
- annotation accuracy
- Higher
CVAT for Autonomous Driving & ADAS Perception
CVAT turns camera, LiDAR, and radar drives into validated training data for the corner cases behind perception failures, trusted in production by autonomy teams like FERNRIDE.
Use cases
Turn Drive Data Into Safer Perception
Autonomous driving perception and 3D sensor fusion
Track road agents in 2D camera frames and 3D LiDAR and radar point clouds.
Lane, road, and drivable surface segmentation
Isolate drivable free space, lane markers, and boundaries.
Traffic sign and signal recognition
Detect and catalogue thousands of local traffic assets.
Driver and in-cabin monitoring
Track eye gaze, head pose, and drowsiness signals.
Edge case mining and ADAS validation
Annotate rare scenarios, adverse weather, and near-miss events.
How it Works
Integrate Seamlessly
Start labeling without changing how you work
Connect your storage, labels, and review stages as they are. Nothing to re-plumb.
Book a demoProjects/urban-drive-q3
Search sourcesAdd source
SourceStatus
1s3://fleet-logs/cam_frontSyncing…
2gs://lidar-archive/topSyncing…
3azure://radar/frontSyncing…
4labels_v4 · KITTI 1.0Syncing…
5Annotation → Validation → AcceptanceSyncing…
6webhook update:job → trainingSyncing…
7labeling_guidelines_v3.pdfSyncing…
Accelerate Validation
Clear labeling backlogs faster
AI pre-labels every frame. Your team or our experts fix what it misses.
Book a demoJobs/Frame 412
van #1
truck #2
pedestrian #3
cyclist #4
Ship with Confidence
Catch label errors before they reach training
Every job is scored against ground truth, so only clean data reaches training.
Book a demoTasks/Quality control
Mean annotation quality
98.4%
Target 95%
Honeypots
CVAT is how ML teams ship automotive AI.
