1 citations · 2 across the 10 of their papers we have counts for
11 papers
DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction
Alexander Prutsch, David Schinagl, Horst Possegger
Advanced automation is a key technology for enhancing the safety of ground operations amidst the increasing density of commercial air traffic. While motion forecasting is a well-st…
Learn to Rank: Visual Attribution by Learning Importance Ranking
David Schinagl, Christian Fruhwirth-Reisinger, Alexander Prutsch +2
Interpreting the decisions of complex computer vision models is crucial to establish trust and accountability, especially in safety-critical domains. An established approach to int…
SHARP: Short-Window Streaming for Accurate and Robust Prediction in Motion Forecasting
Alexander Prutsch, Christian Fruhwirth-Reisinger, David Schinagl +1
In dynamic traffic environments, motion forecasting models must be able to accurately estimate future trajectories continuously. Streaming-based methods are a promising solution, b…
ASCENT: Transformer-Based Aircraft Trajectory Prediction in Non-Towered Terminal Airspace
Alexander Prutsch, David Schinagl, Horst Possegger
Accurate trajectory prediction can improve General Aviation safety in non-towered terminal airspace, where high traffic density increases accident risk. We present ASCENT, a lightw…
Streaming Real-Time Trajectory Prediction Using Endpoint-Aware Modeling
Alexander Prutsch, David Schinagl, Horst Possegger
Future trajectories of neighboring traffic agents have a significant influence on the path planning and decision-making of autonomous vehicles. While trajectory forecasting is a we…
STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving
Christian Fruhwirth-Reisinger, Dušan Malić, Wei Lin +3
We introduce STSBench, a scenario-based framework to benchmark the holistic understanding of vision-language models (VLMs) for autonomous driving. The framework automatically mines…
GACE: Geometry Aware Confidence Enhancement for Black-Box 3D Object Detectors on LiDAR-Data
David Schinagl, Georg Krispel, Christian Fruhwirth-Reisinger +2
Widely-used LiDAR-based 3D object detectors often neglect fundamental geometric information readily available from the object proposals in their confidence estimation. This is most…
MAELi: Masked Autoencoder for Large-Scale LiDAR Point Clouds
Georg Krispel, David Schinagl, Christian Fruhwirth-Reisinger +2
The sensing process of large-scale LiDAR point clouds inevitably causes large blind spots, i.e. regions not visible to the sensor. We demonstrate how these inherent sampling proper…
OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data
David Schinagl, Georg Krispel, Horst Possegger +2
While 3D object detection in LiDAR point clouds is well-established in academia and industry, the explainability of these models is a largely unexplored field. In this paper, we pr…
MURAUER: Mapping Unlabeled Real Data for Label AUstERity
Georg Poier, Michael Opitz, David Schinagl +1
Data labeling for learning 3D hand pose estimation models is a huge effort. Readily available, accurately labeled synthetic data has the potential to reduce the effort. However, to…
Learning Pose Specific Representations by Predicting Different Views
Georg Poier, David Schinagl, Horst Bischof
The labeled data required to learn pose estimation for articulated objects is difficult to provide in the desired quantity, realism, density, and accuracy. To address this issue, w…