Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Matthias Woehrle"'
Publikováno v:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a74908a43efb15d781be93f7b3e8fd3
https://doi.org/10.1109/iros47612.2022.9981309
https://doi.org/10.1109/iros47612.2022.9981309
Publikováno v:
Deep Neural Networks and Data for Automated Driving ISBN: 9783031012327
Verification and validation (V&V) is a crucial step for the certification and deployment of deep neural networks (DNNs). Neuron coverage, inspired by code coverage in software testing, has been proposed as one such V&V method. We provide a summary of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2944bfb6e892cae40c1ac36920a05568
https://doi.org/10.1007/978-3-031-01233-4_14
https://doi.org/10.1007/978-3-031-01233-4_14
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:
2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::504aab8d980ae236ccbbd45ffec88af9
https://doi.org/10.1109/issrew53611.2021.00069
https://doi.org/10.1109/issrew53611.2021.00069
Publikováno v:
ITSC
Threat metrics are used to measure the criticality of traffic situations. Such metrics rely on models predicting future behavior of other traffic participants. These models need to be calculated with respect to lanes for handling oncoming traffic, bu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a29c24cfe03e25547a52609b01762d40
https://doi.org/10.1109/itsc48978.2021.9564790
https://doi.org/10.1109/itsc48978.2021.9564790
Publikováno v:
CVPR Workshops
Whenever a visual perception system is employed in safety-critical applications such as automated driving, a thorough, task-oriented experimental evaluation is necessary to guarantee safe system behavior. While most standard evaluation methods in com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2f122d088fb473c436f519d2dabd2af
https://doi.org/10.1109/cvprw53098.2021.00013
https://doi.org/10.1109/cvprw53098.2021.00013
Publikováno v:
CVPR Workshops
Deep learning-based approaches have gained popularity for environment perception tasks such as semantic segmentation and object detection from images. However, the different nature of a data-driven deep neural nets (DNN) to conventional software is a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::45f1f5f18d8af936d18d214dfdefb5f8
https://doi.org/10.1109/cvprw50498.2020.00170
https://doi.org/10.1109/cvprw50498.2020.00170
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
Autor:
Simone Schuler, Nikolaos Kekatos, Dejan Nickovic, Matthias Woehrle, Alexander Walsch, Goran Frehse, Jens Oehlerking
Publikováno v:
ACC
In this paper, we provide a toolchain that facilitates the integration of formal verification techniques into model-based design. Applying verification tools to industrially relevant models requires three main ingredients: a formal model, a formal ve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f790df45195b5eeafb1d3bd0d7e01d3
https://doi.org/10.23919/acc.2018.8431324
https://doi.org/10.23919/acc.2018.8431324