7 papers
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…
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…
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…
GOOSE: Goal-Conditioned Reinforcement Learning for Safety-Critical Scenario Generation
Joshua Ransiek, Johannes Plaum, Jacob Langner +1
Scenario-based testing is considered state-of-the-art for verifying and validating Advanced Driver Assistance Systems (ADASs) and Automated Driving Systems (ADSs). However, the pra…
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…