218 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Universität UlmDE13 papers
- Esslingen University of Applied SciencesDE5 papers
- Leiden UniversityNL5 papers
- Karlsruhe Institute of TechnologyDE4 papers
- Saarland UniversityDE4 papers
- University of StuttgartDE4 papers
- University of TübingenDE4 papers
- Forschungszentrum JülichDE3 papers
- RheinMain University of Applied SciencesDE3 papers
- University of Applied Sciences LeidenNL3 papers
- University of GöttingenDE3 papers
- University of SiegenDE3 papers
10 papers · 1 filter
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series
Lucas Correia, Jan-Christoph Goos, Thomas Bäck +1
Benchmarking anomaly detection approaches for multivariate time series is a challenging task due to a lack of high-quality datasets. Current publicly available datasets are too sma…
EMPERROR: A Flexible Generative Perception Error Model for Probing Self-Driving Planners
Niklas Hanselmann, Simon Doll, Marius Cordts +2
To handle the complexities of real-world traffic, learning planners for self-driving from data is a promising direction. While recent approaches have shown great progress, they typ…
Tell me what I need to know: Exploring LLM-based (Personalized) Abstractive Multi-Source Meeting Summarization
Frederic Kirstein, Terry Ruas, Robert Kratel +1
Meeting summarization is crucial in digital communication, but existing solutions struggle with salience identification to generate personalized, workable summaries, and context un…
WARP-LCA: Efficient Convolutional Sparse Coding with Locally Competitive Algorithm
Geoffrey Kasenbacher, Felix Ehret, Gerrit Ecke +1
The locally competitive algorithm (LCA) can solve sparse coding problems across a wide range of use cases. Recently, convolution-based LCA approaches have been shown to be highly e…
Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions
Lucas Correia, Jan-Christoph Goos, Philipp Klein +2
Time-series anomaly detection plays an important role in engineering processes, like development, manufacturing and other operations involving dynamic systems. These processes can…
Landmark-based Vehicle Self-Localization Using Automotive Polarimetric Radars
Fabio Weishaupt, Julius F. Tilly, Nils Appenrodt +3
Automotive self-localization is an essential task for any automated driving function. This means that the vehicle has to reliably know its position and orientation with an accuracy…
TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data
Lucas Correia, Jan-Christoph Goos, Philipp Klein +2
As attention to recorded data grows in the realm of automotive testing and manual evaluation reaches its limits, there is a growing need for automatic online anomaly detection. Thi…
Statistical Modelling of Driving Scenarios in Road Traffic using Fleet Data of Production Vehicles
Christian Reichenbächer, Jochen Hipp, Oliver Bringmann
Ensuring the safety of road vehicles at an acceptable level requires the absence of any unreasonable risk arising from all potential hazards linked to the intended au-tomated drivi…
The Radar Ghost Dataset -- An Evaluation of Ghost Objects in Automotive Radar Data
Florian Kraus, Nicolas Scheiner, Werner Ritter +1
Radar sensors have a long tradition in advanced driver assistance systems (ADAS) and also play a major role in current concepts for autonomous vehicles. Their importance is reasone…
CARISMA: CAR-Integrated Service Mesh Architecture
Kevin Klein, Pascal Hirmer, Steffen Becker
The amount of software in modern cars is increasing continuously with traditional electric/electronic (E/E) architectures reaching their limit when deploying complex applications,…