Autonomous Vehicle Perception
Fueling the next generation of self-driving cars with 200K+ multi-modal, synchronized sensor data points.
The Challenge
A top-tier autonomous vehicle manufacturer needed to train their perception stack for complex urban environments. They required perfectly synchronized multi-modal data (LiDAR, radar, and cameras) covering adverse weather, nighttime driving, and unpredictable construction zones.
Our Solution
Dserve AI orchestrated a comprehensive data pipeline. We processed synchronized LiDAR point clouds, 360-degree camera footage, and radar readings across 50+ challenging driving environments. We applied 3D bounding boxes, lane marking splines, and drivable area segmentation to every frame with millimeter precision.
The Impact
"We delivered over 200,000 individually labeled, synchronized data points in native KITTI and nuScenes formats. The AV company saw a 40% reduction in false-positive object detections during nighttime driving simulations, significantly advancing their deployment timeline."
Sensor Fusion Architecture
Solving the 'Black Ice' Edge Case
One of the most critical failures in the client's original model was identifying black ice at night. Standard RGB cameras failed entirely. By perfectly aligning 3D LiDAR reflectance intensity with long-range radar signatures, our annotated dataset taught the perception stack to identify micro-variations in road surface friction, effectively solving the black ice detection problem with a 94% recall rate.