49 citations · 50 across the 4 of their papers we have counts for
4 papers
A Flow-based Credibility Metric for Safety-critical Pedestrian Detection
Maria Lyssenko, Christoph Gladisch, Christian Heinzemann +2
Safety is of utmost importance for perception in automated driving (AD). However, a prime safety concern in state-of-the art object detection is that standard evaluation schemes ut…
Deep Learning Safety Concerns in Automated Driving Perception
Stephanie Abrecht, Alexander Hirsch, Shervin Raafatnia +1
Recent advances in the field of deep learning and impressive performance of deep neural networks (DNNs) for perception have resulted in an increased demand for their use in automat…
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…
Poster Abstract: If You Have Time, Save Energy with Pull
David Hasenfratz, Andreas Meier, Matthias Woehrle +2
We analyze push and pull for data collection in wireless sensor networks. Most applications to date use the traditional push approach, where nodes transmit sensed data immedi- atel…