6 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…
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…
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…
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…
1001 Ways of Scenario Generation for Testing of Self-driving Cars: A Survey
Barbara Schütt, Joshua Ransiek, Thilo Braun +1
Scenario generation is one of the essential steps in scenario-based testing and, therefore, a significant part of the verification and validation of driver assistance functions and…