5 papers
Investigating Driving Interactions: A Robust Multi-Agent Simulation Framework for Autonomous Vehicles
Marc Kaufeld, Rainer Trauth, Johannes Betz
Current validation methods often rely on recorded data and basic functional checks, which may not be sufficient to encompass the scenarios an autonomous vehicle might encounter. In…
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…
A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving
Rainer Trauth, Alexander Hobmeier, Johannes Betz
This study introduces a novel approach to autonomous motion planning, informing an analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate system.…
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…
Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits
Alexander Wischnewski, Maximilian Geisslinger, Johannes Betz +15
Motorsport has always been an enabler for technological advancement, and the same applies to the autonomous driving industry. The team TUM Auton-omous Motorsports will participate…