Zobrazeno 1 - 10
of 2 062
pro vyhledávání: '"P. Zoellner"'
Autor:
Ochs, Sven, Yazgan, Melih, Polley, Rupert, Schotschneider, Albert, Orf, Stefan, Uecker, Marc, Zipfl, Maximilian, Burger, Julian, Vivekanandan, Abhishek, Amritzer, Jennifer, Zofka, Marc René, Zöllner, J. Marius
As cities strive to address urban mobility challenges, combining autonomous transportation technologies with intelligent infrastructure presents an opportunity to transform how people move within urban environments. Autonomous shuttles are particular
Externí odkaz:
http://arxiv.org/abs/2410.20989
Autor:
Wilflingseder, Christoph, Aberl, Johannes, Navarette, Enrique Prado, Hesser, Günter, Groiss, Heiko, Liedke, Maciej O., Butterling, Maik, Wagner, Andreas, Hirschmann, Eric, Corley-Wiciak, Cedric, Zoellner, Marvin H., Capellini, Giovanni, Fromherz, Thomas, Brehm, Moritz
Germanium (Ge), the next-in-line group-IV material, bears great potential to add functionality and performance to next-generation nanoelectronics and solid-state quantum transport based on silicon (Si) technology. Here, we investigate the direct epit
Externí odkaz:
http://arxiv.org/abs/2410.03295
Autor:
Uecker, Marc, Zöllner, J. Marius
Recently, LiDAR perception methods for autonomous vehicles, powered by deep neural networks have experienced steep growth in performance on classic benchmarks, such as nuScenes and SemanticKITTI. However, there are still large gaps in performance whe
Externí odkaz:
http://arxiv.org/abs/2409.18592
Autor:
Polley, Nikolai, Pavlitska, Svetlana, Boualili, Yacin, Rohrbeck, Patrick, Stiller, Paul, Bangaru, Ashok Kumar, Zöllner, J. Marius
Effective traffic light detection is a critical component of the perception stack in autonomous vehicles. This work introduces a novel deep-learning detection system while addressing the challenges of previous work. Utilizing a comprehensive dataset
Externí odkaz:
http://arxiv.org/abs/2409.07284
Autor:
Mayr, Martin, Dreier, Marcel, Kordon, Florian, Seuret, Mathias, Zöllner, Jochen, Wu, Fei, Maier, Andreas, Christlein, Vincent
The imitation of cursive handwriting is mainly limited to generating handwritten words or lines. Multiple synthetic outputs must be stitched together to create paragraphs or whole pages, whereby consistency and layout information are lost. To close t
Externí odkaz:
http://arxiv.org/abs/2409.00786
Representing diverse and plausible future trajectories of actors is crucial for motion forecasting in autonomous driving. However, efficiently capturing the true trajectory distribution with a compact set is challenging. In this work, we propose a no
Externí odkaz:
http://arxiv.org/abs/2407.20732
In real-world autonomous driving, deep learning models can experience performance degradation due to distributional shifts between the training data and the driving conditions encountered. As is typical in machine learning, it is difficult to acquire
Externí odkaz:
http://arxiv.org/abs/2407.14306
Autor:
Zöllner, Rico, Kämpfer, Burkhard
The holographic Einstein-Maxwell-dilaton model is employed to map state-of-the-art lattice QCD thermodynamics data from the temperature ($T$) axis towards the baryon-chemical potential ($\mu_B$) axis aimed at gaining a warm equation of state (EoS) of
Externí odkaz:
http://arxiv.org/abs/2407.02096
Meta-Reinforcement Learning (Meta-RL) aims to acquire meta-knowledge for quick adaptation to diverse tasks. However, applying these policies in real-world environments presents a significant challenge in balancing rapid adaptability with adherence to
Externí odkaz:
http://arxiv.org/abs/2406.14047
In autonomous driving, the most challenging scenarios can only be detected within their temporal context. Most video anomaly detection approaches focus either on surveillance or traffic accidents, which are only a subfield of autonomous driving. We p
Externí odkaz:
http://arxiv.org/abs/2406.06423