Detecting Corner Case in the Context of Highly Automated Driving
Organic Computing -- Doctoral Dissertation Colloquium 2021, kassel university press, (2022)Abstract
This article introduces a research proposal that aims to define, detect, and evaluate
corner cases in the context of machine learning (ML) systems and highly automated driving.
Corner cases are scenarios or data in a dataset that are recorded only very rarely and therefore
only available in small quantities or not at all in the dataset. However, corner cases are very
important for model training, as they are needed to improve the model performance in these
rare scenarios. On the other hand, in safety- and security-critical applications, such as highly
automated driving, corner case data is extremely important for testing and verifying the ML
system.
The research proposal addresses these challenges and focuses on the definition and categorization
of corner cases. Based on this, ML methods for corner case detection are developed,
focusing on object detection for automated driving and trajectory data for intention detection of
pedestrians and cyclists. Finally, the research proposal addresses corner case metrics and the
evaluation of the developed detection models.