40 citations · 40 across the 10 of their papers we have counts for
10 papers
DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weather
Christof Leitgeb, Thomas Puchleitner, Max Peter Ronecker +1
Reliable and weather-robust perception systems are essential for safe autonomous driving and typically employ multi-modal sensor configurations to achieve comprehensive environment…
RADE-Net: Robust Attention Network for Radar-Only Object Detection in Adverse Weather
Christof Leitgeb, Thomas Puchleitner, Max Peter Ronecker +1
Automotive perception systems are obligated to meet high requirements. While optical sensors such as Camera and Lidar struggle in adverse weather conditions, Radar provides a more…
Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBox
Xavier Diaz, Gianluca D'Amico, Raul Dominguez-Sanchez +3
In recent years, interest in automatic train operations has significantly increased. To enable advanced functionalities, robust vision-based algorithms are essential for perceiving…
Vision Foundation Model Embedding-Based Semantic Anomaly Detection
Max Peter Ronecker, Matthew Foutter, Amine Elhafsi +4
Semantic anomalies are contextually invalid or unusual combinations of familiar visual elements that can cause undefined behavior and failures in system-level reasoning for autonom…
A Data-Centric Approach to 3D Semantic Segmentation of Railway Scenes
Nicolas Münger, Max Peter Ronecker, Xavier Diaz +3
LiDAR-based semantic segmentation is critical for autonomous trains, requiring accurate predictions across varying distances. This paper introduces two targeted data augmentation m…
LiDAR-Guided Monocular 3D Object Detection for Long-Range Railway Monitoring
Raul David Dominguez Sanchez, Xavier Diaz Ortiz, Xingcheng Zhou +4
Railway systems, particularly in Germany, require high levels of automation to address legacy infrastructure challenges and increase train traffic safely. A key component of automa…
Deep Learning-Driven State Correction: A Hybrid Architecture for Radar-Based Dynamic Occupancy Grid Mapping
Max Peter Ronecker, Xavier Diaz, Michael Karner +1
This paper introduces a novel hybrid architecture that enhances radar-based Dynamic Occupancy Grid Mapping (DOGM) for autonomous vehicles, integrating deep learning for state-class…
Dynamic Occupancy Grids for Object Detection: A Radar-Centric Approach
Max Peter Ronecker, Markus Schratter, Lukas Kuschnig +1
Dynamic Occupancy Grid Mapping is a technique used to generate a local map of the environment containing both static and dynamic information. Typically, these maps are primarily ge…
Deep Q-Network Based Decision Making for Autonomous Driving
Max Peter Ronecker, Yuan Zhu
Currently decision making is one of the biggest challenges in autonomous driving. This paper introduces a method for safely navigating an autonomous vehicle in highway scenarios by…
Dual-Weight Particle Filter for Radar-Based Dynamic Bayesian Grid Maps
Max Peter Ronecker, Michael Stolz, Daniel Watzenig
Through constant improvements in recent years radar sensors have become a viable alternative to lidar as the main distancing sensor of an autonomous vehicle. Although robust and wi…