Florian Heidecker
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View article: Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation
Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation Open
The operating environment of a highly automated vehicle is subject to change, e.g., weather, illumination, or the scenario containing different objects and other participants in which the highly automated vehicle has to navigate its passen…
View article: Corner cases in machine learning processes
Corner cases in machine learning processes Open
Applications using machine learning (ML), such as highly autonomous driving, depend highly on the performance of the ML model. The data amount and quality used for model training and validation are crucial. If the model cannot detect and i…
View article: Sampling-based Uncertainty Estimation for an Instance Segmentation Network
Sampling-based Uncertainty Estimation for an Instance Segmentation Network Open
The examination of uncertainty in the predictions of machine learning (ML) models is receiving increasing attention. One uncertainty modeling technique used for this purpose is Monte-Carlo (MC)-Dropout, where repeated predictions are gener…
View article: Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving
Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving Open
Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns. Based on these predictions - and additional contextual info…
View article: Description of Corner Cases in Automated Driving: Goals and Challenges
Description of Corner Cases in Automated Driving: Goals and Challenges Open
Scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous situations, termed corner cases (CC). Since many modules of automated driving systems are based on machine learning (ML), CC are an …
View article: An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving
An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving Open
Systems and functions that rely on machine learning (ML) are the basis of highly automated driving. An essential task of such ML models is to reliably detect and interpret unusual, new, and potentially dangerous situations. The detection o…
View article: Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders
Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders Open
Deep neural networks often suffer from overconfidence which can be partly remedied by improved out-of-distribution detection. For this purpose, we propose a novel approach that allows for the generation of out-of-distribution datasets base…
View article: Out-of-distribution Detection and Generation using Soft Brownian Offset\n Sampling and Autoencoders
Out-of-distribution Detection and Generation using Soft Brownian Offset\n Sampling and Autoencoders Open
Deep neural networks often suffer from overconfidence which can be partly\nremedied by improved out-of-distribution detection. For this purpose, we\npropose a novel approach that allows for the generation of out-of-distribution\ndatasets b…
View article: Knowledge Representations in Technical Systems -- A Taxonomy
Knowledge Representations in Technical Systems -- A Taxonomy Open
The recent usage of technical systems in human-centric environments leads to the question, how to teach technical systems, e.g., robots, to understand, learn, and perform tasks desired by the human. Therefore, an accurate representation of…