5 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…
Disentangling Uncertainty for Safe Social Navigation using Deep Reinforcement Learning
Daniel Flögel, Marcos Gómez Villafañe, Joshua Ransiek +1
Autonomous mobile robots are increasingly used in pedestrian-rich environments where safe navigation and appropriate human interaction are crucial. While Deep Reinforcement Learnin…
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…
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…