6 papers · 1 filter
A Kalman Filter-Based Disturbance Observer for Steer-by-Wire Systems
Nikolai Beving, Jonas Marxen, Steffen Mueller +1
Steer-by-Wire systems replace mechanical linkages, which provide benefits like weight reduction, design flexibility, and compatibility with autonomous driving. However, they are su…
Unsupervised Learning for Detection of Rare Driving Scenarios
Dat Le, Thomas Manhardt, Moritz Venator +1
The detection of rare and hazardous driving scenarios is a critical challenge for ensuring the safety and reliability of autonomous systems. This research explores an unsupervised…
Actron3D: Learning Actionable Neural Functions from Videos for Transferable Robotic Manipulation
Anran Zhang, Hanzhi Chen, Yannick Burkhardt +4
We present Actron3D, a framework that enables robots to acquire transferable 6-DoF manipulation skills from monocular, uncalibrated, RGB-only human demonstration videos. Our key id…
Coherent Online Road Topology Estimation and Reasoning with Standard-Definition Maps
Khanh Son Pham, Christian Witte, Jens Behley +2
Most autonomous cars rely on the availability of high-definition (HD) maps. Current research aims to address this constraint by directly predicting HD map elements from onboard sen…
GaussianFusionOcc: A Seamless Sensor Fusion Approach for 3D Occupancy Prediction Using 3D Gaussians
Tomislav Pavković, Mohammad-Ali Nikouei Mahani, Johannes Niedermayer +1
3D semantic occupancy prediction is one of the crucial tasks of autonomous driving. It enables precise and safe interpretation and navigation in complex environments. Reliable pred…
MIND-Stack: Modular, Interpretable, End-to-End Differentiability for Autonomous Navigation
Felix Jahncke, Johannes Betz
Developing robust, efficient navigation algorithms is challenging. Rule-based methods offer interpretability and modularity but struggle with learning from large datasets, while en…