Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Marvin Klingner"'
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 4, Pp 580-601 (2023)
In this work we present a novel deep learning-based approach to detect and specify map deviations in erroneous or outdated high-definition (HD) maps using both sensor and map data as input to a deep neural network (DNN). We first present our proposed
Externí odkaz:
https://doaj.org/article/f71b1dea95b04e7e9b12351ca5b375b4
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 23:20899-20911
Environment perception in autonomous driving vehicles often heavily relies on deep neural networks (DNNs), which are subject to domain shifts, leading to a significantly decreased performance during DNN deployment. Usually, this problem is addressed
Autor:
Shubhankar Borse, Marvin Klingner, Varun Ravi, Hong Cai, Abdulaziz Almuzairee, Senthil Yogamani, Fatih Porikli
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Publikováno v:
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC).
Autor:
Marvin Klingner, Tim Fingscheidt
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 22:4670-4683
Online performance prediction (or: observation) of deep neural networks (DNNs) in highly automated driving presents an unsolved task until now, as most DNNs are evaluated offline requiring datasets with ground truth labels. In practice, however, DNN
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
Autor:
Marvin Klingner, Tim Fingscheidt
Publikováno v:
Deep Neural Networks and Data for Automated Driving ISBN: 9783031012327
While deep neural networks for environment perception tasks in autonomous driving systems often achieve impressive performance on clean and well-prepared images, their robustness under real conditions, i.e., on images being perturbed with noise patte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d20d0e48da0875f806bfb6e451f5150d
https://doi.org/10.1007/978-3-031-01233-4_5
https://doi.org/10.1007/978-3-031-01233-4_5
Autor:
Marvin Klingner, Konstantin Muller, Mona Mirzaie, Jasmin Breitenstein, Jan-Aike Termohlen, Tim Fingscheidt
The emergence of data-driven machine learning (ML) has facilitated significant progress in many complicated tasks such as highly-automated driving. While much effort is put into improving the ML models and learning algorithms in such applications, li
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::adda65c8899328223cdcb2643cc749f3
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:
ITSC
In this paper, we present a novel method for recognizing pole-like objects in LiDAR point clouds, which is useful for landmark-based localization and high-definition (HD) map generation. Our method utilizes a state-of-the-art deep neural network rely