10 papers
From Big Data to Fast Data: Towards High-Quality Datasets for Machine Learning Applications from Closed-Loop Data Collection
Philipp Reis, Jacqueline Henle, Stefan Otten +1
The increasing capabilities of machine learning models, such as vision-language and multimodal language models, are placing growing demands on data in automotive systems engineerin…
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…
Structuring Automotive Data for Systems Engineering: A Taxonomy-Based Approach
Carl Philipp Hohl, Philipp Reis, Tobias Schürmann +2
Vehicle data is essential for advancing data-driven development throughout the automotive lifecycle, including requirements engineering, design, verification, and validation, and p…
A Data-Driven Novelty Score for Diverse In-Vehicle Data Recording
Philipp Reis, Joshua Ransiek, David Petri +2
High-quality datasets are essential for training robust perception systems in autonomous driving. However, real-world data collection is often biased toward common scenes and objec…
Data Quality Matters: Quantifying Image Quality Impact on Machine Learning Performance
Christian Steinhauser, Philipp Reis, Hubert Padusinski +2
Precise perception of the environment is essential in highly automated driving systems, which rely on machine learning tasks such as object detection and segmentation. Compression…
A Framework for a Capability-driven Evaluation of Scenario Understanding for Multimodal Large Language Models in Autonomous Driving
Tin Stribor Sohn, Philipp Reis, Maximilian Dillitzer +3
Multimodal large language models (MLLMs) hold the potential to enhance autonomous driving by combining domain-independent world knowledge with context-specific language guidance. T…
Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Review
Joshua Ransiek, Philipp Reis, Tobias Schürmann +1
Despite advancements in perception and planning for autonomous vehicles (AVs), validating their performance remains a significant challenge. The deployment of planning algorithms i…
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…