Sensor Fusion Annotation Services for Multimodal ADAS and Autonomous Driving Systems

Modern autonomous driving and advanced driver assistance systems rely on sensor fusion to interpret the road environment with greater accuracy than any single sensor can provide. Combining LiDAR, cameras, radar, and sometimes ultrasonic or GPS data enables models to understand depth, geometry, appearance, velocity, and motion patterns at the same time. To build fusion ready datasets, annotations must be consistent, aligned, and structurally compatible across all sensor modalities.DataVLab provides sensor fusion annotation services that integrate 2D and 3D labeling workflows into a unified pipeline. Our annotators follow guidelines designed to maintain cross sensor alignment, coordinate frame accuracy, consistent class definitions, and temporal synchronization. We support calibration based mapping between camera and LiDAR, depth extraction from stereo systems, radar velocity mapping, and annotation aligned to ego vehicle motion.Tasks include 2D and 3D object labeling, cross modality tracking, depth enhanced annotation, occlusion management, fused segmentation, region classification, and multi sensor sequence labeling. We also support labeling for high density LiDAR combined with multi camera rigs, as well as synthetic and simulated environments.Quality control includes spatial alignment checks across coordinate frames, temporal consistency validation, identity tracking, and fusion integrity checks that ensure labels remain coherent across all modalities. Sensitive automotive datasets can be processed under GDPR aligned workflows with optional EU only annotation.Sensor fusion annotation strengthens the performance of perception models by ensuring every object is accurately represented in both 2D and 3D and throughout entire sequences.