no code implementations • 6 Feb 2024 • Daniel Bogdoll, Jing Qin, Moritz Nekolla, Ahmed Abouelazm, Tim Joseph, J. Marius Zöllner
Reinforcement Learning is a highly active research field with promising advancements.
no code implementations • 20 Nov 2023 • Daniel Bogdoll, Yitian Yang, J. Marius Zöllner
Learning unsupervised world models for autonomous driving has the potential to improve the reasoning capabilities of today's systems dramatically.
no code implementations • 18 Sep 2023 • Daniel Bogdoll, Svetlana Pavlitska, Simon Klaus, J. Marius Zöllner
Anomalies in the domain of autonomous driving are a major hindrance to the large-scale deployment of autonomous vehicles.
no code implementations • 10 Aug 2023 • Daniel Bogdoll, Lukas Bosch, Tim Joseph, Helen Gremmelmaier, Yitian Yang, J. Marius Zöllner
We provide a characterization of world models and relate individual components to previous works in anomaly detection to facilitate further research in the field.
no code implementations • 23 May 2023 • Ferdinand Mütsch, Helen Gremmelmaier, Nicolas Becker, Daniel Bogdoll, Marc René Zofka, J. Marius Zöllner
Simulation is an integral part in the process of developing autonomous vehicles and advantageous for training, validation, and verification of driving functions.
1 code implementation • 6 Feb 2023 • Daniel Bogdoll, Svenja Uhlemeyer, Kamil Kowol, J. Marius Zöllner
Deep neural networks (DNN) which are employed in perception systems for autonomous driving require a huge amount of data to train on, as they must reliably achieve high performance in all kinds of situations.
no code implementations • 13 Jul 2022 • Daniel Bogdoll, Meng Zhang, Maximilian Nitsche, J. Marius Zöllner
As a safety-critical problem, however, anomaly detection is a huge hurdle towards a large-scale deployment of autonomous vehicles in the real world.
no code implementations • 10 May 2022 • Julian Wörmann, Daniel Bogdoll, Christian Brunner, Etienne Bührle, Han Chen, Evaristus Fuh Chuo, Kostadin Cvejoski, Ludger van Elst, Philip Gottschall, Stefan Griesche, Christian Hellert, Christian Hesels, Sebastian Houben, Tim Joseph, Niklas Keil, Johann Kelsch, Mert Keser, Hendrik Königshof, Erwin Kraft, Leonie Kreuser, Kevin Krone, Tobias Latka, Denny Mattern, Stefan Matthes, Franz Motzkus, Mohsin Munir, Moritz Nekolla, Adrian Paschke, Stefan Pilar von Pilchau, Maximilian Alexander Pintz, Tianming Qiu, Faraz Qureishi, Syed Tahseen Raza Rizvi, Jörg Reichardt, Laura von Rueden, Alexander Sagel, Diogo Sasdelli, Tobias Scholl, Gerhard Schunk, Gesina Schwalbe, Hao Shen, Youssef Shoeb, Hendrik Stapelbroek, Vera Stehr, Gurucharan Srinivas, Anh Tuan Tran, Abhishek Vivekanandan, Ya Wang, Florian Wasserrab, Tino Werner, Christian Wirth, Stefan Zwicklbauer
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.
1 code implementation • 3 May 2022 • Daniel Bogdoll, Enrico Eisen, Maximilian Nitsche, Christin Scheib, J. Marius Zöllner
Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads.
1 code implementation • 7 Feb 2022 • Daniel Bogdoll, Moritz Nekolla, Tim Joseph, J. Marius Zöllner
Driving on roads is restricted by various traffic rules, aiming to ensure safety for all traffic participants.
1 code implementation • 3 Feb 2022 • Daniel Bogdoll, Felix Schreyer, J. Marius Zöllner
In this paper, we present ad-datasets, an online tool that provides such an overview for more than 150 data sets.
1 code implementation • 5 Nov 2021 • Daniel Bogdoll, Johannes Jestram, Jonas Rauch, Christin Scheib, Moritz Wittig, J. Marius Zöllner
Due to the massive volume of sensor data that must be sent in real-time, highly efficient data compression is elementary to prevent an overload of network infrastructure.
no code implementations • 20 Sep 2021 • Daniel Bogdoll, Jasmin Breitenstein, Florian Heidecker, Maarten Bieshaar, Bernhard Sick, Tim Fingscheidt, J. Marius Zöllner
Scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous situations, termed corner cases (CC).