A Flow-based Credibility Metric for Safety-critical Pedestrian Detection

no code implementations • 12 Feb 2024 • Maria Lyssenko, Christoph Gladisch, Christian Heinzemann, Matthias Woehrle, Rudolph Triebel

Safety is of utmost importance for perception in automated driving (AD).

object-detection Object Detection +2

Deep Learning Safety Concerns in Automated Driving Perception

no code implementations • 7 Sep 2023 • Stephanie Abrecht, Alexander Hirsch, Shervin Raafatnia, Matthias Woehrle

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 automated driving (AD) systems.

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

no code implementations • 29 Apr 2021 • Sebastian Houben, Stephanie Abrecht, Maram Akila, Andreas Bär, Felix Brockherde, Patrick Feifel, Tim Fingscheidt, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Ahmed Hammam, Anselm Haselhoff, Felix Hauser, Christian Heinzemann, Marco Hoffmann, Nikhil Kapoor, Falk Kappel, Marvin Klingner, Jan Kronenberger, Fabian Küppers, Jonas Löhdefink, Michael Mlynarski, Michael Mock, Firas Mualla, Svetlana Pavlitskaya, Maximilian Poretschkin, Alexander Pohl, Varun Ravi-Kumar, Julia Rosenzweig, Matthias Rottmann, Stefan Rüping, Timo Sämann, Jan David Schneider, Elena Schulz, Gesina Schwalbe, Joachim Sicking, Toshika Srivastava, Serin Varghese, Michael Weber, Sebastian Wirkert, Tim Wirtz, Matthias Woehrle

Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods.

Autonomous Driving BIG-bench Machine Learning