61 citations · 157 across the 16 of their papers we have counts for
18 papers
Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation
Florian Heidecker, Ahmad El-Khateeb, Maarten Bieshaar +1
The operating environment of a highly automated vehicle is subject to change, e.g., weather, illumination, or the scenario containing different objects and other participants in wh…
A Safety-Adapted Loss for Pedestrian Detection in Automated Driving
Maria Lyssenko, Piyush Pimplikar, Maarten Bieshaar +2
In safety-critical domains like automated driving (AD), errors by the object detector may endanger pedestrians and other vulnerable road users (VRU). As common evaluation metrics a…
Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving
Kevin Rösch, Florian Heidecker, Julian Truetsch +5
Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns.…
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,…