ISHMAM Jahin completed Machine Learning for Automotive Safety

Built a complete perception-validation dossier for a synthetic Aurora-7 detector: defined detection rigorously with an IoU ≥ 0.5 match rule, computed aggregate and VRU-specific metrics from a scored table, and showed that VRU false negatives—not headline accuracy—drive the safety case. They then sliced recall by operating condition, identified the night+occluded VRU collapse, wrote ISO 21448-style functional-insufficiency mitigations, and issued a consistent conditional deployment recommendation grounded in their own evidence.

• Leakage-Free Detection Definition
• Safety-Critical Metric Selection
• Per-Condition Failure Triage
• SOTIF Functional-Insufficiency Reasoning