61 citations · 111 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders
Felix Möller, Diego Botache, Denis Huseljic +3
Deep neural networks often suffer from overconfidence which can be partly remedied by improved out-of-distribution detection. For this purpose, we propose a novel approach that all…
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,…