Abdullah Rahim Hussain completed Machine Learning for Automotive Safety

Built a perception validation dossier that defined detection rigorously with IoU≥0.5, computed aggregate and VRU-specific metrics from a scored synthetic table, and surfaced the night/occluded recall collapse that drives safety risk. The final release recommendation was evidence-based and internally consistent, with a real-world case analysis that linked the Uber Tempe crash to the same vulnerable-road-user night-miss failure mode.

• Leakage-Free Detection Definition
• Safety-Critical Metric Selection
• Per-Condition Failure Triage
• Evidence-Based Release Recommendation