Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Stephanie Abrecht"'
Autor:
Christian Heinzemann, Stephanie Abrecht, Christoph Gladisch, Matthias Woehrle, Konrad Groh, Lydia Gauerhof
Publikováno v:
ACM Transactions on Cyber-Physical Systems. 5:1-28
Due to the impressive performance of deep neural networks (DNNs) for visual perception, there is an increased demand for their use in automated systems. However, to use deep neural networks in practice, novel approaches are needed, e.g., for testing.
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
Autor:
Sebastian Houben, Stephanie Abrecht, Konrad Groh, Matthias Woehrle, Christian Heinzemann, Maram Akila, Sujan Sai Gannamaneni
Publikováno v:
Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops ISBN: 9783030555825
SAFECOMP Workshops
SAFECOMP Workshops
The use of neural networks in perception pipelines of autonomous systems such as autonomous driving is indispensable due to their outstanding performance. But, at the same time their complexity poses a challenge with respect to safety. An important q
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c671539fa348d1a3c3f06978f3e99cd8
https://doi.org/10.1007/978-3-030-55583-2_21
https://doi.org/10.1007/978-3-030-55583-2_21
Publikováno v:
Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops ISBN: 9783030555825
SAFECOMP Workshops
SAFECOMP Workshops
Deep learning methods are widely regarded as indispensable when it comes to designing perception pipelines for autonomous agents such as robots, drones or automated vehicles. The main reasons, however, for deep learning not being used for autonomous
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::256e09925de3f76d7f95d4de6d525112
https://doi.org/10.1007/978-3-030-55583-2_25
https://doi.org/10.1007/978-3-030-55583-2_25