Automotive & Mobility
Perception Data Precise Enough to Certify — Not Just Demo
Level 4 autonomy doesn't fail in the lab — it fails in monsoon rain at 2am. Nextura builds the annotated datasets that make perception models robust across every driving condition, traffic pattern, and regulatory regime your vehicle will encounter.
What we deliver here.
LiDAR & Point Cloud
3D cuboid annotation, point cloud segmentation, and LiDAR-camera sensor fusion labeling by annotators trained in AV perception schema, not generic image labelers.
Adverse Condition Datasets
Rain, fog, night, glare, and dust — a 6-condition taxonomy ensuring your model doesn't degrade when conditions get real. Covers Indian and European traffic scenes.
HD Map Annotation
Lane marking, traffic sign classification, road boundary labeling, and semantic segmentation of urban and highway scenes for high-definition mapping pipelines.
In-Cabin AI
Driver monitoring system (DMS) datasets — drowsiness detection, gaze estimation, emotion labeling, and occupant classification for cabin intelligence models.
EV & Charging
Battery health signal annotation, charging anomaly tagging, and grid interaction dataset labeling for electric vehicle management AI.
V2X Communication
Vehicle-to-infrastructure signal labeling, pedestrian intent annotation, and edge-case scenario generation for cooperative driving systems.
Results that survive production.
Ready to ship Automotive & Mobility AI that earns its place in production?
Tell us your model, your data gaps, and your deadline. We'll scope a pilot dataset that proves Nextura's quality before you commit to scale.