ADAS and AD functionalities are built around AI-based perception components. To be safely deployed, these must reliably perform across the Operational Design Domain (ODD). However, challenges remain in collecting the right data for AI training and testing. Data collection and enrichment processes should concentrate on
scenarios representing weak spots of AI perception models. This implies a need for a feedback loop from AI development to data collection. This talk will discuss solutions to these challenges derived from joint projects done with OEMs and insights from developing the AI safety validation tool aidkit.
aidkit facilitates ODD-based performance analyses of perception models with the help of diverse data augmentation techniques.