7 papers
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…
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…
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…