Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches

Abstract

Automated driving has become a major topic of interest not only in the active research community but also in mainstream media reports. Visual perception of such intelligent vehicles has experienced large progress in the last decade thanks to advances in deep learning techniques but some challenges still remain. One such challenge is the detection of corner cases. They are unexpected and unknown situations that occur while driving. Conventional visual perception methods are often not able to detect them because corner cases have not been witnessed during training. Hence, their detection is highly safety-critical, and detection methods can be applied to vast amounts of collected data to select suitable training data. A reliable detection of corner cases will not only further automate the data selection procedure and increase safety in autonomous driving but can thereby also affect the public acceptance of the new technology in a positive manner. In this work, we continue a previous systematization of corner cases on different levels by an extended set of examples for each level. Moreover, we group detection approaches into different categories and link them with the corner case levels. Hence, we give directions to showcase specific corner cases and basic guidelines on how to technically detect them.

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Cited by in corpus (5)

• Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors
• An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving
• Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems
• Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving
• Conditioning Latent-Space Clusters for Real-World Anomaly Classification