51 citations · 338 across the 88 of their papers we have counts for
6 papers · 1 filter
Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?
Hanno Gottschalk, Matthias Rottmann, Maida Saltagic
While automated driving is often advertised with better-than-human driving performance, this work reviews that it is nearly impossible to provide direct statistical evidence on the…
Special cosmological models derived from the semiclassical Einstein equation on flat FLRW space-times
Hanno Gottschalk, Nicolai Rothe, Daniel Siemssen
This article presents numerical work on a special case of the cosmological semiclassical Einstein equation (SCE). The SCE describes the interaction of relativistic quantum matter b…
Generative Modeling of Turbulence
Claudia Drygala, Benjamin Winhart, Francesca di Mare +1
We present a mathematically well founded approach for the synthetic modeling of turbulent flows using generative adversarial networks (GAN). Based on the analysis of chaotic, deter…
False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation
Pascal Colling, Matthias Rottmann, Lutz Roese-Koerner +1
We present a novel post-processing tool for semantic segmentation of LiDAR point cloud data, called LidarMetaSeg, which estimates the prediction quality segmentwise. For this purpo…
Background-Foreground Segmentation for Interior Sensing in Automotive Industry
Claudia Drygala, Matthias Rottmann, Hanno Gottschalk +2
To ensure safety in automated driving, the correct perception of the situation inside the car is as important as its environment. Thus, seat occupancy detection and classification…
Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors
Tobias Riedlinger, Matthias Rottmann, Marius Schubert +1
The vast majority of uncertainty quantification methods for deep object detectors such as variational inference are based on the network output. Here, we study gradient-based epist…