106 citations
- German Research Centre for Artificial IntelligenceDE7 papers
- University of KaiserslauternDE6 papers
- Technische Universität BerlinDE4 papers
- Max Planck Institute for Intelligent SystemsDE3 papers
- RWTH Aachen UniversityDE2 papers
- Durham UniversityGB1 paper
- Fraunhofer Institute for Industrial EngineeringDE1 paper
- Fraunhofer Institute for Manufacturing Engineering and AutomationDE1 paper
- German University in CairoEG1 paper
- Institut für NanophotonikDE1 paper
- Johannes Kepler University of LinzAT1 paper
- Karlsruhe University of EducationDE1 paper
9 papers · 1 filter
Vectorized Scenario Description and Motion Prediction for Scenario-Based Testing
Max Winkelmann, Constantin Vasconi, Steffen Müller
Automated vehicles (AVs) are tested in diverse scenarios, typically specified by parameters such as velocities, distances, or curve radii. To describe scenarios uniformly independe…
A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction
Amin E. Bakhshipour, Alireza Koochali, Ulrich Dittmer +3
Despite various breakthroughs in machine learning and data analysis techniques for improving smart operation and management of urban water infrastructures, some key limitations obs…
Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain
Alireza Koochali, Maria Walch, Sankrutyayan Thota +3
Generative models are designed to address the data scarcity problem. Even with the exploding amount of data, due to computational advancements, some applications (e.g., health care…
On Controller Tuning with Time-Varying Bayesian Optimization
Paul Brunzema, Alexander von Rohr, Sebastian Trimpe
Changing conditions or environments can cause system dynamics to vary over time. To ensure optimal control performance, controllers should adapt to these changes. When the underlyi…
Random Noise vs State-of-the-Art Probabilistic Forecasting Methods : A Case Study on CRPS-Sum Discrimination Ability
Alireza Koochali, Peter Schichtel, Andreas Dengel +1
The recent developments in the machine learning domain have enabled the development of complex multivariate probabilistic forecasting models. Therefore, it is pivotal to have a pre…
Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios
Max Winkelmann, Mike Kohlhoff, Hadj Hamma Tadjine +1
To validate the safety of automated vehicles (AV), scenario-based testing aims to systematically describe driving scenarios an AV might encounter. In this process, continuous input…