Automotive / Robotics

*Overview DataClap offers specialized sensor fusion annotation services tailored for autonomous vehicles and robotics. Combining multi-sensor data such as camera, radar, and LiDAR, we create high-quality labeled datasets essential for training and validating perception systems. Our annotations support safe navigation, object detection, and motion planning in complex environments. Sensor Fusion Annotation: Precise labeling of camera, radar, and LiDAR data for full environment perception. Lane, Vehicle, Pedestrian Labeling: Accurate marking of lanes, vehicles, pedestrians, and key objects. Trajectory & Motion Prediction: Annotated paths and predicted motions for dynamic object tracking. Deliverables Annotated datasets in common formats (JSON, XML, CSV) compatible with autonomous system training pipelines Detailed metadata, including sensor type, timestamp synchronization, and environmental conditions Quality control reports and inter-annotator agreement metrics Versioned datasets for iterative model development Documentation of annotation guidelines and labeling conventions Security & Compliance Data is processed under encrypted storage, private S3 buckets, and role-based access protocols. Optional compliance packages available for regulated industry datasets.

Integrations & Scalability Full compatibility with AWS S3, SageMaker, and other cloud-based ML platforms Scalable workflows capable of processing large volumes of sensor fusion data Seamless integration into simulation environments and perception model training pipelines API support for real-time annotation updates and feedback loops Use Cases Developing perception and sensor fusion models for autonomous vehicles Enhancing safety in ADAS (Advanced Driver Assistance Systems) Robotics navigation and obstacle detection in complex terrains Trajectory prediction for planning and collision avoidance Creating benchmark datasets for perception system validation *