A Semi-Automated Corner Case Detection and Evaluation Pipeline
Isabelle Tulleners Tobias Moers Thomas Schulik Martin Sedlacek
In order to deploy automated vehicles to the public, it has to be proven that
the vehicle can safely and robustly handle traffic in many different scenarios.
One important component of automated vehicles is the perception system that
captures and processes the environment around the vehicle. Perception systems
require large datasets for training their deep neural network. Knowing which
parts of the data in these datasets describe a corner case is an advantage
during training or testing of the network. These corner cases describe
situations that are rare and potentially challenging for the network. We
propose a pipeline that converts collective expert knowledge descriptions into
the extended KI Absicherung ontology. The ontology is used to describe scenes
and scenarios that can be mapped to perception datasets. The corner cases can
then be extracted from the datasets. In addition, the pipeline enables the
evaluation of the detection networks against the extracted corner cases to
measure their performance.