Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?

Overview

The study examines whether redundant perception networks justify reduced testing through error independence. Experiments using separate training configurations and multi-view MNIST show correlated failures, cautioning against assuming independent errors in redundancy arguments.