Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Patrick Feifel"'
Publikováno v:
Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2cda77d5a8d2be33f441c3260a5705b9
https://doi.org/10.5220/0011690900003417
https://doi.org/10.5220/0011690900003417
Autor:
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
Publikováno v:
Deep Neural Networks and Data for Automated Driving ISBN: 9783031012327
Fingscheidt, Gottschalk et al. (Hg.): Deep Neural Networks and Data for Automated Driving. Robustness, Uncertainty Quantification, and Insights Towards Safety
Fingscheidt, Gottschalk et al. (Hg.): Deep Neural Networks and Data for Automated Driving. Robustness, Uncertainty Quantification, and Insights Towards Safety
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 shortcomings are diverse and range from a lack of generalization over i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a97320425a5b7fac20b0272cdc9fe19b
https://doi.org/10.1007/978-3-031-01233-4_1
https://doi.org/10.1007/978-3-031-01233-4_1
Publikováno v:
CSCS
The automation of driving systems relies on proof of the correct functioning of perception. Arguing the safety of deep neural networks (DNNs) must involve quantifiable evidence. Currently, the application of DNNs suffers from an incomprehensible beha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e20f7c039072dc5678dea33ce6bfeb7
https://doi.org/10.1145/3488904.3493379
https://doi.org/10.1145/3488904.3493379
Publikováno v:
CVPR Workshops
AI-based perception is a key factor towards the automation of driving systems. A conclusive safety argumentation must provide evidence for safe functioning. Existing safety standards are not suitable to deal with non-interpretable deep neural network
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::37841e49e1afdfb43ba2bf8095079fe7
https://doi.org/10.1109/cvprw53098.2021.00012
https://doi.org/10.1109/cvprw53098.2021.00012