This talk introduces a safety framework for machine learning in Advanced Driver Assistance Systems (ADAS), addressing gaps in ISO 26262 and ISO 21448. It covers three pillars: domain-centric dataset design, a novel pipeline for robust out-of-model scope detection, and neural criticality analysis to quantify safety relevance of individual neurons. Together, these methods strengthen the link between AI performance and functional safety, advancing the reliability of ADAS.