8 papers
A Domain-Specific Language for LLM-Driven Trigger Generation in Multimodal Data Collection
Philipp Reis, Philipp Rigoll, Martin Zehetner +3
Data-driven systems depend on task-relevant data, yet data collection pipelines remain passive and indiscriminate. Continuous logging of multimodal sensor streams incurs high stora…
Generative AI in Systems Engineering: A Framework for Risk Assessment of Large Language Models
Stefan Otten, Philipp Reis, Philipp Rigoll +4
The increasing use of Large Language Models (LLMs) offers significant opportunities across the engineering lifecycle, including requirements engineering, software development, proc…
A Feedback-Control Framework for Efficient Dataset Collection from In-Vehicle Data Streams
Philipp Reis, Philipp Rigoll, Christian Steinhauser +2
Modern AI systems are increasingly constrained not by model capacity but by the quality and diversity of their data. Despite growing emphasis on data-centric AI, most datasets are…
Disentangling Coordiante Frames for Task Specific Motion Retargeting in Teleoperation using Shared Control and VR Controllers
Max Grobbel, Daniel Flögel, Philipp Rigoll +1
Task performance in terms of task completion time in teleoperation is still far behind compared to humans conducting tasks directly. One large identified impact on this is the huma…
Behavior Forests: Real-Time Discovery of Dynamic Behavior for Data Selection
Philipp Reis, Philipp Rigoll, Eric Sax
Automated Driving Systems (ADS) development relies on utilizing real-world vehicle data. The volume of data generated by modern vehicles presents transmission, storage, and computa…
CLIPping the Limits: Finding the Sweet Spot for Relevant Images in Automated Driving Systems Perception Testing
Philipp Rigoll, Laurenz Adolph, Lennart Ries +1
Perception systems, especially cameras, are the eyes of automated driving systems. Ensuring that they function reliably and robustly is therefore an important building block in the…
Unveiling Objects with SOLA: An Annotation-Free Image Search on the Object Level for Automotive Data Sets
Philipp Rigoll, Jacob Langner, Eric Sax
Huge image data sets are the fundament for the development of the perception of automated driving systems. A large number of images is necessary to train robust neural networks tha…
Focus on the Challenges: Analysis of a User-friendly Data Search Approach with CLIP in the Automotive Domain
Philipp Rigoll, Patrick Petersen, Hanno Stage +2
Handling large amounts of data has become a key for developing automated driving systems. Especially for developing highly automated driving functions, working with images has beco…