ADAS Front‑Camera Validation 6x Faster at Half the Cost

A leading North American Tier‑1 supplier needed to validate front‑camera performance for lanes, signs, and lights on a new EV program. Their existing approach relied on heavy manual effort, multi‑month timelines, and fragmented tools to reach stable KPI results. By implementing Ottometric’s AI‑powered validation pipeline, the team cut end‑to‑end time to KPIs from months to weeks while reducing total validation cost by roughly half.

Challenge

Ottometric connected the Tier‑1’s existing data collection, cloud, and HiL infrastructure into a single governed pipeline for front‑camera KPIs. Annotation, quality control, and KPI computation for lanes, signs, and lights were automated so engineers could focus on interpreting results rather than assembling them. The solution provided consistent KPI outputs across iterations, with clear traceability back to underlying data and scenarios, enabling faster, more confident validation decisions.

Approach

Ottometric introduced an automated homologation toolchain that integrates with the customer’s existing validation processes. The toolchain automates KPI creation with built‑in quality checks, flags likely false negatives, and exposes results through a Natural Language Query interface for intuitive data exploration. It also provides full visualization of sensor and system outputs, along with automated weekly reporting and ground truth generation, which together cut manual effort and significantly speed up validation cycles.​

Results

With Ottometric, the Tier‑1 achieved stable KPI delivery in about 1 month instead of 6, giving engineering and program teams much earlier visibility into performance. Automated pipelines replaced multiple ad‑hoc scripts and manual reviews, reducing risk of errors and making it easier to repeat and extend validation for future releases.

Impact

The new validation flow gave the Tier‑1 a repeatable way to reach trusted front‑camera KPIs far earlier in the program. Automated data handling, annotation, and KPI computation freed engineers from manual assembly work and allowed them to focus on understanding behavior and fixing issues. The team could now run more iterations with less effort, improving both coverage and confidence before SOP.​

Conclusion

By integrating Ottometric’s automated validation pipeline, the Tier‑1 transformed front‑camera ADAS validation from a slow, resource‑heavy process into a fast, scalable capability. Cutting end‑to‑end time from months to days, halving cost, and achieving a 75% efficiency gain in validation resources created a durable advantage for current and future EV programs.