AI Data Labeling for Autonomous Vehicles
Enterprise-grade annotation platform for self-driving cars. Handle LiDAR point clouds, camera fusion, 3D cuboids, and safety-critical edge cases at scale.
Autonomous Vehicle Challenges
Self-driving technology requires unprecedented annotation scale, precision, and safety validation.
Scale at Millions of Frames
Process petabytes of sensor data from continuous testing with high throughput and accuracy.
Multi-Sensor Fusion
Synchronize and annotate across LiDAR, camera, radar, and GPS data streams simultaneously.
Edge Cases & Safety
Identify and annotate rare but critical scenarios that determine safety certification.
Purpose-Built AV Annotation
3D Cuboid Annotation
Annotate vehicles, pedestrians, and objects in 3D space with precise position, rotation, and dimensions.
- Auto-interpolation
- Ground plane fitting
- Occlusion handling
LiDAR + Camera Fusion
Synchronized annotation across point clouds and camera feeds with automatic projection.
- Sensor calibration
- Multi-modal consistency
- Cross-view validation
Lane & Road Marking
Segment lane lines, curbs, crosswalks, and road boundaries with sub-pixel precision.
- Polyline tools
- Dashed line support
- HD map integration
Traffic Sign Detection
Classify and localize traffic signs, signals, and road markings across all conditions.
- 100+ sign classes
- Attribute tagging
- Visibility scoring
Advanced Capabilities
Sequential Frame Linking
Track objects across video sequences with automatic ID propagation and trajectory validation.
Weather & Lighting Conditions
Tag environmental factors including rain, fog, night driving, and glare scenarios.
Sensor Calibration QA
Built-in validation for sensor alignment, timestamp sync, and coordinate transformation.
Edge Case Mining
AI-powered detection of anomalies, near-misses, and safety-critical scenarios.
Leading AV Company
"TigerLabel's 3D annotation tools and quality pipeline helped us reach L4 certification 6 months ahead of schedule."
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