Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Jonas Löhdefink"'
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031250552
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7343ee9e4be11202a0d0389e3ca0f114
https://doi.org/10.1007/978-3-031-25056-9_40
https://doi.org/10.1007/978-3-031-25056-9_40
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:
Bernhard Sick, Maarten Bieshaar, Jasmin Breitenstein, Christoph Stiller, Tim Fingscheidt, Florian Heidecker, Kevin Rosch, Jonas Löhdefink
Publikováno v:
IV
Systems and functions that rely on machine learning (ML) are the basis of highly automated driving. An essential task of such ML models is to reliably detect and interpret unusual, new, and potentially dangerous situations. The detection of those sit
Autor:
Nikhil Kapoor, Andreas Bär, Peter Schlicht, Tim Fingscheidt, Jonas Löhdefink, Serin Varghese, Fabian Hüger
Enabling autonomous driving (AD) can be considered one of the biggest challenges in today's technology. AD is a complex task accomplished by several functionalities, with environment perception being one of its core functions. Environment perception
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::466b67779210ddecc054d72751b21a2b
Autor:
Serin Varghese, Jonas Löhdefink, Nikhil Kapoor, Nico M. Schmidt, Chun Yuan, Tim Fingscheidt, Roland Zimmerman, Peter Schlicht, Fabian Hüger
Publikováno v:
CSCS
Deep neural networks are often not robust to semantically-irrelevant changes in the input. In this work we address the issue of robustness of state-of-the-art deep convolutional neural networks (CNNs) against commonly occurring distortions in the inp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5af9a622335a631fea0a61670758a14c
http://arxiv.org/abs/2012.01386
http://arxiv.org/abs/2012.01386
Publikováno v:
2020 IEEE Intelligent Vehicles Symposium (IV).
Cooperative perception with many sensors involved greatly improves the performance of perceptual systems in autonomous vehicles. However, the increasing amount of sensor data leads to a bottleneck due to limited capacity of vehicle-to-X (V2X) communi
Autor:
Fabian Hüger, Jonas Löhdefink, Nico M. Schmidt, Peter Schlicht, Tim Fingscheidt, Justin Fehrling, Marvin Klingner
Publikováno v:
CVPR Workshops
Autonomous driving requires self awareness of its perception functions. Technically spoken, this can be realized by observers, which monitor the performance indicators of various perception modules. In this work we choose, exemplarily, a semantic seg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e69553347aaadd4e598e0504bd5830e
Publikováno v:
2019 IEEE Intelligent Vehicles Symposium (IV).
The high amount of sensors required for autonomous driving poses enormous challenges on the capacity of automotive bus systems. There is a need to understand tradeoffs between bitrate and perception performance. In this paper, we compare the image co