61 citations · 131 across the 6 of their papers we have counts for
6 papers
Foundation Models for Amodal Video Instance Segmentation in Automated Driving
Jasmin Breitenstein, Franz Jünger, Andreas Bär +1
In this work, we study amodal video instance segmentation for automated driving. Previous works perform amodal video instance segmentation relying on methods trained on entirely la…
On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models
Marvin Klingner, Konstantin Müller, Mona Mirzaie +3
The emergence of data-driven machine learning (ML) has facilitated significant progress in many complicated tasks such as highly-automated driving. While much effort is put into im…
Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baseline
Jasmin Breitenstein, Tim Fingscheidt
Amodal perception terms the ability of humans to imagine the entire shapes of occluded objects. This gives humans an advantage to keep track of everything that is going on, especia…
Description of Corner Cases in Automated Driving: Goals and Challenges
Daniel Bogdoll, Jasmin Breitenstein, Florian Heidecker +4
Scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous situations, termed corner cases (CC). Since many modules of automated dri…
An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving
Florian Heidecker, Jasmin Breitenstein, Kevin Rösch +5
Systems and functions that rely on machine learning (ML) are the basis of highly automated driving. An essential task of such ML models is to reliably detect and interpret unusual,…
Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches
Jasmin Breitenstein, Jan-Aike Termöhlen, Daniel Lipinski +1
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 vehicle…