Autonomous Vehicle Data Annotation Services

Autonomous Vehicles

Training data for vehicles that can't afford to be wrong

Synnth annotates the sensor data behind perception, prediction, and planning systems — from single-camera dashcam footage to multi-sensor LiDAR and radar fusion datasets — so your AV stack sees the road the way it actually is.

Overview

Autonomous driving models are only as good as the edge cases they’ve seen. Rain-slicked intersections, occluded pedestrians, unmarked rural roads, and construction zones rarely show up in generic datasets — but they’re where perception systems fail. Synnth builds and labels scenario-specific driving datasets, combining real-world capture campaigns with frame-accurate annotation from a team trained on automotive-grade labeling standards.

What we do

Data collection & annotation across every AI modality

From sourcing raw data to delivering production-ready labeled datasets — Synnth covers the full pipeline across the four core data types that power modern AI.

Data collection

Multi-camera and dashcam capture rigs across urban, highway, and rural routes
LiDAR and radar sensor recording, synchronized with camera feeds
Scenario-based driving campaigns (weather, lighting, traffic density, road type)
Pedestrian, cyclist, and animal edge-case sourcing
Simulated and synthetic scenario augmentation for rare events

Annotation

Lane and road boundary detection
2D and 3D bounding boxes for vehicles, pedestrians, and obstacles
LiDAR point cloud segmentation and object annotation
Pedestrian and cyclist tracking across frames
Traffic sign and signal classification
Multi-sensor fusion labeling (camera + LiDAR + radar alignment)
Free-space and drivable-area segmentation
Temporal event tagging (lane changes, near-misses, right-of-way conflicts)

How we work

A process built for repeatability and trust

Every Synnth project follows the same structured pipeline, regardless of data type, language, or volume. That consistency is not bureaucratic — it is the mechanism through which we maintain quality at scale.

Frame-consistent tracking at scale. Object IDs are held consistent across thousands of consecutive frames, with inter-annotator agreement checks built into every batch — critical for training prediction models that depend on temporal continuity.

Sensor fusion expertise. Our annotators work natively across camera, LiDAR, and radar data together, not as separate pipelines bolted on afterward — so your fused-sensor labels are actually aligned in 3D space.

Edge-case sourcing on demand. Need more night-driving footage, unprotected left turns, or occluded-pedestrian scenarios? We run targeted collection campaigns to fill the gaps in your existing dataset rather than reshuffling what you already have.

99.2% QA pass rate on safety-critical labels, with senior reviewer sign-off before delivery — because a missed pedestrian in a training set is not a rounding error.

Delivery formats

2,000+ expert annotators, matched to your domain

KITTI, nuScenes, COCO, Waymo Open Dataset schema, or your custom ontology — exported as JSON, XML, or via API.

Ready to build AI on data you can trust?

Whether you need a small pilot batch or an ongoing production annotation pipeline — tell us what you’re building and we’ll tell you exactly how we can help.

FAQs

Common questions

Everything you need to know before starting a AI Data collection or annotation project with Synnth.

💡 Can’t find your answer here? Talk to our team — we typically respond within one business day.

Do you annotate raw LiDAR point clouds or only camera data?

Both, and in combination. We label 3D point clouds directly and align annotations across LiDAR, radar, and camera streams for sensor fusion training.

Can you source driving footage for specific conditions we're missing?

Yes. We run scenario-based capture campaigns — night driving, adverse weather, specific road geometries — to fill gaps identified in your existing dataset.

How do you maintain object consistency across video frames?

Trained annotators track object IDs frame-by-frame with automated consistency checks, backed by inter-annotator agreement review before any batch ships.

Get started

Start your AI Data collection or annotation project today

Tell us your use case, action taxonomy, environment, and volume. Our team responds within one business day with a scoping plan and no-obligation quote.