Big Data und Machine Learning
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Uncertainty Estimation of LiDAR Scene Semantic Segmentation (DFG i.c.sens)Despite the capability of advanced deep learning models to accurately assign semantic labels to LiDAR point clouds, there is a notable lack of methods for uncertainty quantification. However, the estimation of uncertainty is essential for assessing the reliability of any prediction, particularly for safety-critical systems such as autonomous vehicles that rely on real data, including LiDAR point clouds. These systems need not only to perceive their surroundings but also to quantify uncertainty to avoid over-reliance on potentially erroneous predictions. Two primary types of uncertainty are generally distinguished: epistemic and aleatoric. Epistemic uncertainty, which arises from the model itself, reflects the reliability of a model’s predictions, whereas aleatoric uncertainty stems from characteristics inherent in the data.Leitung: apl. Prof. Claus BrennerTeam:Jahr: 2022Förderung: DFG Graduiertenkolleg i.c.sens
Laserscanning
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Uncertainty Estimation of LiDAR Scene Semantic Segmentation (DFG i.c.sens)Despite the capability of advanced deep learning models to accurately assign semantic labels to LiDAR point clouds, there is a notable lack of methods for uncertainty quantification. However, the estimation of uncertainty is essential for assessing the reliability of any prediction, particularly for safety-critical systems such as autonomous vehicles that rely on real data, including LiDAR point clouds. These systems need not only to perceive their surroundings but also to quantify uncertainty to avoid over-reliance on potentially erroneous predictions. Two primary types of uncertainty are generally distinguished: epistemic and aleatoric. Epistemic uncertainty, which arises from the model itself, reflects the reliability of a model’s predictions, whereas aleatoric uncertainty stems from characteristics inherent in the data.Leitung: apl. Prof. Claus BrennerTeam:Jahr: 2022Förderung: DFG Graduiertenkolleg i.c.sens
