Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Sebastian Houben"'
Autor:
Sebastian Houben
Publikováno v:
ELCVIA Electronic Letters on Computer Vision and Image Analysis, Vol 15, Iss 2 (2016)
Modern vehicles are deployed with a large number of sensors in order to provide a rich spectrum of driver assistance functionality. These systems enhance security and comfort of passengers and other traffic participants alike, but they also pave the
Externí odkaz:
https://doaj.org/article/643a92f3e94d4c1e83d49b54f8d802f4
This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence.Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges
Publikováno v:
RoboCup 2022: ISBN: 9783031284687
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::db1cf157a27bbd133e177d886d87e10c
https://doi.org/10.1007/978-3-031-28469-4_7
https://doi.org/10.1007/978-3-031-28469-4_7
Publikováno v:
EMS Annual Meeting 2022, 4-9 September 2022, Bonn, Germany
EMS Annual Meeting Abstracts Vol. 19, EMS2022-505, 2022
EMS Annual Meeting Abstracts Vol. 19, EMS2022-505, 2022
The accurate forecasting of solar radiation plays an important role for predictive control applications for energy systems with a high share of photovoltaic (PV) energy. Especially off-grid microgrid applications using predictive control applications
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1305bb8089d31af81b8ab590e692314a
https://doi.org/10.5194/ems2022-505
https://doi.org/10.5194/ems2022-505
Autor:
Dorina Weichert, Alexander Kister, Sebastian Houben, Peter Volbach, Marcus Trost, Johannes Hartung, Alexander Bergner, Stefan Wrobel
Publikováno v:
SSRN Electronic Journal.
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/CVF International Conference on Computer Vision Workshops (ICCVW).
Publikováno v:
2021 IEEE Intelligent Vehicles Symposium (IV).
The precise 3D localization of non-ego vehicles is a crucial task for the long-term goal of autonomous driving. In urban scenarios, where pedestrians frequently interact with vehicles, this task also requires a precise modeling of dynamic vehicle par
Publikováno v:
2021 IEEE Intelligent Vehicles Symposium (IV).
The utilization of automatically generated image training data is a feasible way to enhance existing datasets, e.g., by strengthening underrepresented classes or by adding new lighting or weather conditions for more variety. Synthetic images can also
Autor:
Linara Adilova, Maram Akila, Sebastian Houben, Jan David Schneider, Elena Schulz, Fabian Hüger, Tim Wirtz
Publikováno v:
CVPR Workshops
Data-driven sensor interpretation in autonomous driving can lead to highly implausible predictions as can most of the time be verified with common-sense knowledge. However, learning common knowledge only from data is hard and approaches for knowledge