Zobrazeno 1 - 10
of 248
pro vyhledávání: '"Bogdoll A"'
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
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
Dealing with atypical traffic scenarios remains a challenging task in autonomous driving. However, most anomaly detection approaches cannot be trained on raw sensor data but require exposure to outlier data and powerful semantic segmentation models t
Externí odkaz:
http://arxiv.org/abs/2406.06370
Autor:
Bogdoll, Daniel, Hamdard, Iramm, Rößler, Lukas Namgyu, Geisler, Felix, Bayram, Muhammed, Wang, Felix, Imhof, Jan, de Campos, Miguel, Tabarov, Anushervon, Yang, Yitian, Gottschalk, Hanno, Zöllner, J. Marius
The scale-up of autonomous vehicles depends heavily on their ability to deal with anomalies, such as rare objects on the road. In order to handle such situations, it is necessary to detect anomalies in the first place. Anomaly detection for autonomou
Externí odkaz:
http://arxiv.org/abs/2405.07865
Autor:
Bogdoll, Daniel, Qin, Jing, Nekolla, Moritz, Abouelazm, Ahmed, Joseph, Tim, Zöllner, J. Marius
Reinforcement Learning is a highly active research field with promising advancements. In the field of autonomous driving, however, often very simple scenarios are being examined. Common approaches use non-interpretable control commands as the action
Externí odkaz:
http://arxiv.org/abs/2402.04168
Autor:
Ochs, Sven, Doll, Jens, Grimm, Daniel, Fleck, Tobias, Heinrich, Marc, Orf, Stefan, Schotschneider, Albert, Gremmelmaier, Helen, Polley, Rupert, Pavlitska, Svetlana, Zipfl, Maximilian, Schneider, Helen, Mütsch, Ferdinand, Bogdoll, Daniel, Kuhnt, Florian, Schörner, Philip, Zofka, Marc René, Zöllner, J. Marius
Most automated driving functions are designed for a specific task or vehicle. Most often, the underlying architecture is fixed to specific algorithms to increase performance. Therefore, it is not possible to deploy new modules and algorithms easily.
Externí odkaz:
http://arxiv.org/abs/2404.02645
The European Green Deal aims to achieve climate neutrality by 2050, which demands improved emissions efficiency from the transportation industry. This study uses an agent-based simulation to analyze the sustainability impacts of shared autonomous shu
Externí odkaz:
http://arxiv.org/abs/2311.14118
Learning unsupervised world models for autonomous driving has the potential to improve the reasoning capabilities of today's systems dramatically. However, most work neglects the physical attributes of the world and focuses on sensor data alone. We p
Externí odkaz:
http://arxiv.org/abs/2311.11762
Anomalies in the domain of autonomous driving are a major hindrance to the large-scale deployment of autonomous vehicles. In this work, we focus on high-resolution camera data from urban scenes that include anomalies of various types and sizes. Based
Externí odkaz:
http://arxiv.org/abs/2309.09676
Autor:
Bogdoll, Daniel, Bosch, Lukas, Joseph, Tim, Gremmelmaier, Helen, Yang, Yitian, Zöllner, J. Marius
In recent years there have been remarkable advancements in autonomous driving. While autonomous vehicles demonstrate high performance in closed-set conditions, they encounter difficulties when confronted with unexpected situations. At the same time,
Externí odkaz:
http://arxiv.org/abs/2308.05701