22 citations · 54 across the 13 of their papers we have counts for
13 papers
Validate the Dream Before You Trust Its Verdict: Admissibility for World-Model Simulators
Christian Oefinger, Finn Rasmus Schäfer, Korbinian Moller +2
Across robotics, World Models (WMs) are increasingly used to evaluate action policies by simulating the consequences of actions in an imagined world, and returning a success or saf…
Disengagement Analysis and Field Tests of a Prototypical Open-Source Level 4 Autonomous Driving System
Marvin Seegert, Christian Oefinger, Korbinian Moller +2
Proprietary Autonomous Driving Systems are typically evaluated through disengagements, unplanned manual interventions to alter vehicle behavior, as annually reported by the Califor…
Towards Safe Autonomous Driving: A Real-Time Motion Planning Algorithm on Embedded Hardware
Korbinian Moller, Glenn Johannes Tungka, Lucas Jürgens +1
Ensuring the functional safety of Autonomous Vehicles (AVs) requires motion planning modules that not only operate within strict real-time constraints but also maintain controllabi…
Reinforcement Learning-based Dynamic Adaptation for Sampling-Based Motion Planning in Agile Autonomous Driving
Alexander Langmann, Yevhenii Tokarev, Mattia Piccinini +2
Sampling-based trajectory planners are widely used for agile autonomous driving due to their ability to generate fast, smooth, and kinodynamically feasible trajectories. However, t…
Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
Korbinian Moller, Roland Stroop, Mattia Piccinini +2
Sampling-based motion planning is a well-established approach in autonomous driving, valued for its modularity and analytical tractability. In complex urban scenarios, however, uni…
Towards Safe Autonomous Driving: A Real-Time Safeguarding Concept for Motion Planning Algorithms
Korbinian Moller, Rafael Neher, Marvin Seegert +1
Ensuring the functional safety of motion planning modules in autonomous vehicles remains a critical challenge, especially when dealing with complex or learning-based software. Onli…
Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
Yuan Gao, Mattia Piccinini, Yuchen Zhang +12
For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing hav…
MultiDrive: A Co-Simulation Framework Bridging 2D and 3D Driving Simulation for AV Software Validation
Marc Kaufeld, Korbinian Moller, Alessio Gambi +2
Scenario-based testing using simulations is a cornerstone of Autonomous Vehicles (AVs) software validation. So far, developers needed to choose between low-fidelity 2D simulators t…
Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios
Korbinian Moller, Truls Nyberg, Jana Tumova +1
Motion planning in uncertain environments like complex urban areas is a key challenge for autonomous vehicles (AVs). The aim of our research is to investigate how AVs can navigate…
From Shadows to Safety: Occlusion Tracking and Risk Mitigation for Urban Autonomous Driving
Korbinian Moller, Luis Schwarzmeier, Johannes Betz
Autonomous vehicles (AVs) must navigate dynamic urban environments where occlusions and perception limitations introduce significant uncertainties. This research builds upon and ex…
From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios
Yuan Gao, Mattia Piccinini, Korbinian Moller +2
Ensuring the safety of autonomous vehicles requires virtual scenario-based testing, which depends on the robust evaluation and generation of safety-critical scenarios. So far, rese…
Overcoming Blind Spots: Occlusion Considerations for Improved Autonomous Driving Safety
Korbinian Moller, Rainer Trauth, Johannes Betz
Our work introduces a module for assessing the trajectory safety of autonomous vehicles in dynamic environments marked by high uncertainty. We focus on occluded areas and occluded…
FRENETIX: A High-Performance and Modular Motion Planning Framework for Autonomous Driving
Rainer Trauth, Korbinian Moller, Gerald Wuersching +1
Our research introduces a modular motion planning framework for autonomous vehicles using a sampling-based trajectory planning algorithm. This approach effectively tackles the chal…