Early Detection of Dataset Insufficiencies in ADAS
Catch ADAS dataset gaps before they become safety risks
Why read this white paper:
- Link dataset gaps to AI errors and hazards
- Align datasets with ISO 8800 safety properties
- Define stronger requirements based on the ODD
- Improve traceability, balance and labeling quality
- Reduce rework and strengthen confidence in ADAS data
Who is this white paper for:
This whitepaper is written for senior professionals working on safety-relevant AI, ADAS perception, computer vision and dataset development.
It is especially relevant for:
- AI Safety / ML Safety Leads
- ADAS / Autonomous Perception Technical Leads
- Computer Vision Engineering Leads
- Dataset / Data Engineering Leads
- Engineering Directors / VPs of Autonomy
- Functional Safety / FuSa Managers
- SOTIF / Safety Case Engineers
- Quality Assurance and Validation Managers
- Annotation, Data Review and Dataset Quality Owners
- Technical Product Owners working on AI-based perception systems
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