12 papers · 1 filter
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…