7.1k citations
- European Space Operations CentreDE8 papers
- Technical University of MunichDE8 papers
- Universität UlmDE8 papers
- Centre National de la Recherche ScientifiqueFR6 papers
- European Space Astronomy CentreES6 papers
- European Space Research and Technology CentreNL6 papers
- Karlsruhe Institute of TechnologyDE6 papers
- Technische Universität DresdenDE6 papers
- University of BremenDE5 papers
- University of StuttgartDE5 papers
- Centro de AstrobiologíaES4 papers
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)DE4 papers
6 papers · 1 filter
An Enhanced Graph Representation for Machine Learning Based Automatic Intersection Management
Marvin Klimke, Jasper Gerigk, Benjamin Völz +1
The improvement of traffic efficiency at urban intersections receives strong research interest in the field of automated intersection management. So far, mostly non-learning algori…
Hierarchies of Planning and Reinforcement Learning for Robot Navigation
Jan Wöhlke, Felix Schmitt, Herke van Hoof
Solving robotic navigation tasks via reinforcement learning (RL) is challenging due to their sparse reward and long decision horizon nature. However, in many navigation tasks, high…
Budget-based real-time Executor for Micro-ROS
Jan Staschulat, Ralph Lange, Dakshina Narahari Dasari
The Robot Operating System (ROS) is a popular robotics middleware framework. In the last years, it underwent a redesign and reimplementation under the name ROS~2. It now features Q…
Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving
Di Feng, Yifan Cao, Lars Rosenbaum +2
This work presents a probabilistic deep neural network that combines LiDAR point clouds and RGB camera images for robust, accurate 3D object detection. We explicitly model uncertai…
Learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning
Holger Banzhaf, Paul Sanzenbacher, Ulrich Baumann +1
Sampling-based motion planning is an effective tool to compute safe trajectories for automated vehicles in complex environments. However, a fast convergence to the optimal solution…
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector
Di Feng, Xiao Wei, Lars Rosenbaum +2
Training a deep object detector for autonomous driving requires a huge amount of labeled data. While recording data via on-board sensors such as camera or LiDAR is relatively easy,…