# Towards Corner Case Detection for Autonomous Driving

## TL;DR

This paper proposes a system framework for detecting critical corner cases in video data from moving vehicles, addressing robustness issues in autonomous driving systems by identifying unusual and potentially dangerous situations.

## Contribution

It introduces a formal definition of corner cases and a versatile system framework for online and offline detection from vehicle-mounted cameras, extending beyond fixed-camera scenarios.

## Key findings

- Framework effectively detects corner cases in driving videos.
- Applicable to both online real-time and offline data screening.
- Enhances robustness of autonomous driving systems.

## Abstract

The progress in autonomous driving is also due to the increased availability of vast amounts of training data for the underlying machine learning approaches. Machine learning systems are generally known to lack robustness, e.g., if the training data did rarely or not at all cover critical situations. The challenging task of corner case detection in video, which is also somehow related to unusual event or anomaly detection, aims at detecting these unusual situations, which could become critical, and to communicate this to the autonomous driving system (online use case). Such a system, however, could be also used in offline mode to screen vast amounts of data and select only the relevant situations for storing and (re)training machine learning algorithms. So far, the approaches for corner case detection have been limited to videos recorded from a fixed camera, mostly for security surveillance. In this paper, we provide a formal definition of a corner case and propose a system framework for both the online and the offline use case that can handle video signals from front cameras of a naturally moving vehicle and can output a corner case score.