Explainable Autonomous Vehicle Decisions (XAVD)
Explainability pipeline for autonomous driving decisions using BDD100K, MetaDrive, and Grad-CAM.
XAVD studies why an autonomous driving model makes specific decisions in traffic-heavy scenes.
Project highlights:
- Trained a ResNet18-based perception model with BDD100K and evaluated behaviors in MetaDrive simulation.
- Applied Grad-CAM saliency analysis to trace lane markings, pedestrians, and traffic signs that influenced predictions.
- Compared model confidence and interpretable attention maps to identify failure modes and decision inconsistency.
This project helped me connect model performance with transparent reasoning for safety-critical AI systems.