Parth Malasne completed Machine Learning for Automotive Safety

Built a perception validation dossier end to end for a synthetic Aurora-7 detector: defined detections with an IoU >= 0.5 rule, scored precision/recall and VRU false-negative rate from a synthetic table, and sliced recall by night, occlusion, and rarity to expose the worst safety gaps. The final recommendation was evidence-based and conditional, with SOTIF-style mitigations tied to the specific night-and-occlusion pedestrian risk and a correct comparison to the Uber Tempe crash mechanism.

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