Ahmed Osal completed Machine Learning for Automotive Safety

Built a perception-validation dossier that correctly defined detection by IoU, computed precision/recall plus the safety-critical VRU false-negative rate, and then sliced recall by lighting, visibility, and rarity to surface the highest-risk misses. The analysis clearly identified night-time and occluded VRU cases as the weakest slices, showing strong judgment about which metrics matter for safety.

• Leakage-Free Detection Definition
• Safety-Critical Metric Selection
• Per-Condition Failure Analysis