Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Varun Ravi Kumar"'
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 24:3638-3659
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::77990c3c1b5fde0c88a2a9d353ded90c
https://doi.org/10.1109/tits.2023.3235057
https://doi.org/10.1109/tits.2023.3235057
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:
Ahmed Rida Sekkat, Yohan Dupuis, Varun Ravi Kumar, Hazem Rashed, Senthil Yogamani, Pascal Vasseur, Paul Honeine
Surround-view cameras are a primary sensor for automated driving, used for near-field perception. It is one of the most commonly used sensors in commercial vehicles primarily used for parking visualization and automated parking. Four fisheye cameras
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4467977a3e11bd49ede3e4585b2076ee
Publikováno v:
ECMR
In this paper, we present a system for incrementally reconstructing a dense 3D model of the geometry of an outdoor environment using a single monocular camera attached to a moving vehicle. Dense models provide a rich representation of the environment
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61ceef1d5b4d7d62e0da778793ceaf96
http://arxiv.org/abs/2108.07736
http://arxiv.org/abs/2108.07736
Electric Vehicles are increasingly common, with inductive chargepads being considered a convenient and efficient means of charging electric vehicles. However, drivers are typically poor at aligning the vehicle to the necessary accuracy for efficient
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c0f3158d58e8767e3fc6091b3162a69
http://arxiv.org/abs/2105.12763
http://arxiv.org/abs/2105.12763
Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning
Publikováno v:
ITSC
Autonomous driving is rapidly advancing, and Level 2 functions are becoming a standard feature. One of the foremost outstanding hurdles is to obtain robust visual perception in harsh weather and low light conditions where accuracy degradation is seve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9535c207a04f0a715c8ce11136d78998
http://arxiv.org/abs/2104.14042
http://arxiv.org/abs/2104.14042