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
57 papers
Geometric 2D Scene Graph Generation
Christoph Jahn, Urs Waldmann, Bastian Goldluecke
In production processes for consumer products, assembly instructions are essential not only for planning but also for executing the production process. Likewise in robotics, it is…
Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection
Kadir-Kaan Özer, René Ebeling, Markus Enzweiler
Multivariate time series anomalies often manifest as shifts in cross-channel dependencies rather than simple amplitude excursions. In autonomous driving, for instance, a steering c…
Fully Compensated Lines in Ferrimagnets
Qais Ali, Anna Grünebohm, Halil Ibrahim Sözen +3
We generalize the classic Néel diagram and identify another type of ferrimagnetic phase that remains fully magnetically compensated below the Curie temperature, forming a continuou…
Test Case Specification Techniques and System Testing Tools in the Automotive Industry: A Review
Denesa Zyberaj, Pascal Hirmer, Marco Aiello +1
The automotive domain is shifting to software-centric development to meet regulation, market pressure, and feature velocity. This shift increases embedded systems' complexity and s…
STREAM-VAE: Dual-Path Routing for Slow and Fast Dynamics in Vehicle Telemetry Anomaly Detection
Kadir-Kaan Özer, René Ebeling, Markus Enzweiler
Automotive telemetry data exhibits slow drifts and fast spikes, often within the same sequence, making reliable anomaly detection challenging. Standard reconstruction-based methods…
DQS: A Low-Budget Query Strategy for Enhancing Unsupervised Data-driven Anomaly Detection Approaches
Lucas Correia, Jan-Christoph Goos, Thomas Bäck +1
Truly unsupervised approaches for time series anomaly detection are rare in the literature. Those that exist suffer from a poorly set threshold, which hampers detection performance…
Neural Rendering for Sensor Adaptation in 3D Object Detection
Felix Embacher, David Holtz, Jonas Uhrig +2
Autonomous vehicles often have varying camera sensor setups, which is inevitable due to restricted placement options for different vehicle types. Training a perception model on one…
Offline Auto Labeling: BAAS
Stefan Haag, Bharanidhar Duraisamy, Felix Govaers +3
This paper introduces BAAS, a new Extended Object Tracking (EOT) and fusion-based label annotation framework for radar detections in autonomous driving. Our framework utilizes Baye…
Decision-Focused Fine-Tuning of Time Series Foundation Models for Dispatchable Feeder Optimization
Maximilian Beichter, Nils Friederich, Janik Pinter +7
Time series foundation models provide a universal solution for generating forecasts to support optimization problems in energy systems. Those foundation models are typically traine…
Bumpy Ride? Understanding the Effects of External Forces on Spatial Interactions in Moving Vehicles
Markus Sasalovici, Albin Zeqiri, Robin Connor Schramm +6
As the use of Head-Mounted Displays in moving vehicles increases, passengers can immerse themselves in visual experiences independent of their physical environment. However, intera…
Blending the Worlds: World-Fixed Visual Appearances in Automotive Augmented Reality
Robin Connor Schramm, Markus Sasalovici, Jann Philipp Freiwald +3
With the transition to fully autonomous vehicles, non-driving related tasks (NDRTs) become increasingly important, allowing passengers to use their driving time more efficiently. I…
Augmented Journeys: Interactive Points of Interest for In-Car Augmented Reality
Robin Connor Schramm, Ginevra Fedrizzi, Markus Sasalovici +2
As passengers spend more time in vehicles, the demand for non-driving related tasks (NDRTs) increases. In-car Augmented Reality (AR) has the potential to enhance passenger experien…
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,…
Quantum Algorithm for Green's Functions Measurements in the Fermi-Hubbard Model
Gino Bishop, Dmitry Bagrets, Frank K. Wilhelm
In the framework of the hybrid quantum-classical variational cluster approach (VCA) to strongly correlated electron systems one of the goals of a quantum subroutine is to find sing…
MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing
Lucas Correia, Jan-Christoph Goos, Philipp Klein +2
A clear need for automatic anomaly detection applied to automotive testing has emerged as more and more attention is paid to the data recorded and manual evaluation by humans reach…