4 citations · 4 across the 4 of their papers we have counts for
5 papers
MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Driving
Thomas Steinecker, Denis Trescher, Alexander Bienemann +2
Reinforcement learning constitutes a promising approach owing to its potential for superhuman performance and self-learned policies. However, its application to real-world autonomo…
Dynamics-Decoupled Trajectory Alignment for Sim-to-Real Transfer in Reinforcement Learning for Autonomous Driving
Thomas Steinecker, Alexander Bienemann, Denis Trescher +2
Reinforcement learning (RL) has shown promise in robotics, but deploying RL on real vehicles remains challenging due to the complexity of vehicle dynamics and the mismatch between…
Knowledge Distillation for Semantic Segmentation: A Label Space Unification Approach
Anton Backhaus, Thorsten Luettel, Mirko Maehlisch
An increasing number of datasets sharing similar domains for semantic segmentation have been published over the past few years. But despite the growing amount of overall data, it i…
Excavating in the Wild: The GOOSE-Ex Dataset for Semantic Segmentation
Raphael Hagmanns, Peter Mortimer, Miguel Granero +2
The successful deployment of deep learning-based techniques for autonomous systems is highly dependent on the data availability for the respective system in its deployment environm…
Collision Probability Distribution Estimation via Temporal Difference Learning
Thomas Steinecker, Thorsten Luettel, Mirko Maehlisch
We introduce CollisionPro, a pioneering framework designed to estimate cumulative collision probability distributions using temporal difference learning, specifically tailored to a…