Major Automotive OEM Scaled ADAS Perception Pipeline
How multi-sensor annotation for LiDAR, radar, and camera data across diverse driving conditions trained next-generation ADAS perception models at scale.
Multi-Sensor
LiDAR, Radar, Camera Fusion
1M+
Frames Annotated
4
Continent Coverage
The Challenge
A major automotive OEM developing advanced driver-assistance systems (ADAS) needed to train perception models that could reliably detect and classify objects in real-world driving conditions across multiple continents. Their challenges included:
- •Fusing data from LiDAR, radar, and camera sensors into consistent annotations
- •Handling diverse driving conditions: weather, lighting, and road types across continents
- •Scaling annotation to over one million frames while maintaining quality
- •Safety-critical requirements demanding extremely high annotation accuracy
The Solution
SwarmLearn deployed a multi-sensor annotation pipeline capable of handling LiDAR point clouds, radar returns, and camera imagery simultaneously. Our approach included:
Multi-Sensor Fusion Annotation
Annotated objects across LiDAR, radar, and camera data with temporal consistency, enabling the model to leverage complementary sensor strengths.
Global Condition Coverage
Covered diverse driving scenarios across four continents, including rain, snow, fog, night driving, and varying road infrastructure.
Scalable Quality Assurance
Implemented multi-stage review with automotive domain experts to ensure annotation accuracy met safety-critical standards.
Continuous Delivery Pipeline
Built an agile annotation pipeline that could ingest new sensor data and deliver annotated datasets on tight engineering timelines.
The Results
The multi-sensor annotation pipeline delivered measurable improvements to the OEM's ADAS perception systems:
1M+ Frames Annotated Across 4 Continents
Achieved massive scale with consistent quality across diverse geographic and environmental conditions.
Improved Multi-Sensor Fusion Accuracy
Consistent cross-sensor annotations significantly improved the reliability of fused perception outputs.
Reduced Edge-Case Failures
Extensive coverage of rare and challenging scenarios reduced failure rates in adverse weather and low-light conditions.
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