74 citations · 360 across the 51 of their papers we have counts for
7 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…
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…
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis
Julia Rosenzweig, Eduardo Brito, Hans-Ulrich Kobialka +8
Many machine learning applications can benefit from simulated data for systematic validation - in particular if real-life data is difficult to obtain or annotate. However, since si…
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Sebastian Houben, Stephanie Abrecht, Maram Akila +38
The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These s…
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
Robin Chan, Krzysztof Lis, Svenja Uhlemeyer +6
State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes. As such, they are ill-equipped to handle pre…