LIKITHA C RAI completed Machine Learning for Automotive Safety

Built a perception validation dossier that defined detection correctly at IoU >= 0.5, scored a synthetic detections table, and separated headline accuracy from the safety-critical VRU false-negative rate. The candidate then sliced recall by lighting, visibility, and rarity, surfaced the worst night/occluded VRU failures, and translated them into ISO 21448-style hazard chains with concrete mitigations and an evidence-based ship recommendation.

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