Skip to content
A Top 5 global AI outsourcing company by Outsource Accelerator, above Scale AI.
Industry solution

AI training data for Autonomous vehicles

LiDAR, camera, and sensor-fusion annotation for AV perception: dense 3D labels with scenario-based gold sets and per-frame consensus review.

Start a pilot See related work
Overview

Data operations tuned to Autonomous vehicles

Autonomous driving stacks are only as safe as the perception data behind them. Corpshore AI labels LiDAR point clouds, multi-camera video, and radar returns as a fused whole, so objects stay consistent across sensors and frames. Embedded teams work to your ontology, hold a per-frame consensus review, and mine the long tail of edge cases that decide real-world safety.

3D cuboid and semantic segmentation labelingEdge-case and scenario miningSensor-fusion annotation across LiDAR, camera, and radarField data collection in target driving regions
AI training data for Autonomous vehicles at Corpshore AI
The challenge

Where the data breaks down

  • Point clouds and camera frames drift out of alignment, so a single object gets labeled differently across sensors.
  • Rare events, occlusions, night and adverse weather are underrepresented in fleet data yet drive most safety risk.
  • Frame-to-frame track identity is hard to hold across long sequences, which corrupts trajectory learning.
  • Labeling volume scales into millions of frames, and quality tends to slip as throughput rises.
Our approach

How Corpshore AI delivers

  • We label LiDAR, camera, and radar as a fused scene, so each object carries one identity across every sensor and frame.
  • Scenario-based gold sets seed known edge cases; annotators mine and tag new ones as they surface.
  • A per-frame consensus review compares independent passes and escalates disagreement to senior reviewers.
  • Track identity is verified across sequences before delivery, so trajectories stay coherent for planning.
Delivery workflow

From scope to delivery, in autonomous vehicles

Step through how a autonomous vehicles engagement runs under one operator and a single SLA.

1. Scope

We map your perception ontology, sensor rig, and safety-critical classes, then build scenario-based gold sets for the edge cases that matter.

Stage 1 of 5
Data types

What we deliver

LiDAR 3D cuboids and point-cloud segmentation
2D bounding boxes, polygons, and semantic masks
Lane, drivable-surface, and road-marking geometry
Sensor-fusion tracks with cross-sensor identity
Traffic-sign, signal, and vehicle-state attributes
97%+
accuracy on 3D and 2D labels via the QA cascade
Fused
one object identity across LiDAR, camera, and radar
Edge cases
scenario mining for the long-tail safety events
50 to 70%
cost advantage versus US-domestic labeling
Why Corpshore AI

The operator advantage for autonomous vehicles

  • We operate owned hubs, so throughput and quality stay under one accountable SLA from collection to delivery.
  • Field teams can collect driving data in target regions, not just label what a fleet already captured.
  • The three-tier QA cascade holds accuracy steady as volume scales into millions of frames.
Relevant services

Services behind this solution

Data CollectionField & physical-world dataAnnotation & LabelingEvery modality, 97%+ accuracyRobotics & Embodied AIReal-world sensor & vision data
FAQ

Autonomous vehicles, answered

LiDAR point clouds, multi-camera video, and radar returns, labeled as a fused scene so each object keeps one identity across sensors and frames. We also handle lane geometry, drivable-surface segmentation, and traffic-sign and signal attributes.

Ready to scope a pilot?

Tell us your modality, volume, and languages. We'll return an indicative scope, timeline, and cost band.

Start a pilot Explore careers