Sensor and Video Annotation for an Automotive ADAS Program
An automotive technology company developing advanced driver assistance systems needed high-volume annotation of road scene video and sensor data to train perception models to correctly identify vehicles, pedestrians and road features.
Client snapshot
| Industry | Automotive technology and ADAS |
|---|---|
| Region | Multi-country, matched to the client's road-condition diversity requirements |
| Engagement type | Road scene video and sensor annotation |
The challenge
The client needed annotation volume well beyond its internal team's capacity, across a wide range of road, weather and lighting conditions, with accuracy requirements high enough to support a safety-relevant perception system.
The approach
Jwuma assigned trained annotators to label vehicles, pedestrians, lane markings and road features across the client's road scene dataset, following a detailed annotation specification, with a multi-tier review ladder applied given the safety-relevant nature of the use case and gold-task calibration to catch labeling drift.
Results
The client scaled its annotation throughput well beyond internal capacity while maintaining the accuracy threshold required for its perception model's safety validation process.
Frequently asked questions
Why does ADAS annotation require a higher quality bar than general image labeling?
Because labeling errors can directly affect a safety-relevant perception system, which requires stricter review and calibration than general-purpose annotation work.
What road conditions should ADAS training data cover?
A diverse range of weather, lighting, road type and traffic density conditions, since a model trained on narrow conditions generalizes poorly to the rest.
How is labeling accuracy verified at this scale?
Through multi-tier review escalation and known-answer gold tasks seeded into the annotation queue to catch drift before it reaches the delivered dataset.
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Published 2026-10-02 by Jwuma, operated by Corpshore AI. This case study is an anonymized composite representative of the kind of work Jwuma performs in this industry, described by industry, region and engagement type rather than by company name, since this engagement is not yet cleared for public naming. Discuss a similar program at client.corpshore.ai.