6 papers
Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation
Kerim Turacan, Hannes Reichert, Andrei Bolandut +1
LiDAR semantic segmentation is a core perception capability for autonomous vehicles and mobile robots. However, safe operation also depends on knowing when predictions are unreliab…
DD-MDN: Human Trajectory Forecasting with Diffusion-Based Dual Mixture Density Networks and Uncertainty Self-Calibration
Manuel Hetzel, Kerim Turacan, Hannes Reichert +2
Human Trajectory Forecasting (HTF) predicts future human movements from past trajectories and environmental context, with applications in Autonomous Driving, Smart Surveillance, an…
Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems
Hannes Reichert, Lukas Lang, Kevin Rösch +6
Full-stack autonomous driving perception modules usually consist of data-driven models based on multiple sensor modalities. However, these models might be biased to the sensor setu…
Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans
Hannes Reichert, Benjamin Serfling, Elijah Schüssler +3
In recent studies, numerous previous works emphasize the importance of semantic segmentation of LiDAR data as a critical component to the development of driver-assistance systems a…
LiDAR Based Semantic Perception for Forklifts in Outdoor Environments
Benjamin Serfling, Hannes Reichert, Lorenzo Bayerlein +2
In this study, we present a novel LiDAR-based semantic segmentation framework tailored for autonomous forklifts operating in complex outdoor environments. Central to our approach i…
Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications
Manuel Hetzel, Hannes Reichert, Konrad Doll +1
Autonomous systems, like vehicles or robots, require reliable, accurate, fast, resource-efficient, scalable, and low-latency trajectory predictions to get initial knowledge about f…