White Paper: Early detection of dataset insufficiencies in ADAS
Catch ADAS dataset gaps before they become safety risks
Why read this whitepaper?
Dataset problems are often discovered too late: during model validation, system testing or even after deployment. This whitepaper shows how dataset insufficiencies can contribute to unsafe ADAS behavior, and how teams can detect and mitigate them earlier in the dataset lifecycle.
With this whitepaper, you will learn how to:
Understand how dataset insufficiencies can lead to AI errors and vehicle-level hazards
Connect dataset issues to ISO 8800 data-related safety properties
Define stronger dataset requirements based on the ODD
Use metadata to improve traceability, relevance, balance and dataset independence
Identify common labeling and annotation risks before they affect model performance
Apply data review and dataset safety analysis earlier in development
Reduce late-stage debugging, costly recollection and validation rework
Improve confidence in dataset quality for safety-relevant ADAS perception systems
Who should read this whitepaper?
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
