58 citations · 63 across the 6 of their papers we have counts for
5 papers · 1 filter
Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentation
Mariella Dreissig, Simon Ruehle, Florian Piewak +1
Safety-critical applications such as autonomous driving require robust 3D environment perception algorithms capable of handling diverse and ambiguous surroundings. The predictive p…
On the Calibration of Uncertainty Estimation in LiDAR-based Semantic Segmentation
Mariella Dreissig, Florian Piewak, Joschka Boedecker
The confidence calibration of deep learning-based perception models plays a crucial role in their reliability. Especially in the context of autonomous driving, downstream tasks lik…
On the calibration of underrepresented classes in LiDAR-based semantic segmentation
Mariella Dreissig, Florian Piewak, Joschka Boedecker
The calibration of deep learning-based perception models plays a crucial role in their reliability. Our work focuses on a class-wise evaluation of several model's confidence perfor…
A Survey on Deep Domain Adaptation for LiDAR Perception
Larissa T. Triess, Mariella Dreissig, Christoph B. Rist +1
Scalable systems for automated driving have to reliably cope with an open-world setting. This means, the perception systems are exposed to drastic domain shifts, like changes in we…
Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm
Mariella Dreissig, Mohamed Hedi Baccour, Tim Schaeck +1
Modern advanced driver-assistance systems analyze the driving performance to gather information about the driver's state. Such systems are able, for example, to detect signs of dro…