Search Results for author:
Found 7 papers, 1 papers with code
Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation
We also present our first results of an iterative training cycle that outperforms the baseline and where the data added to the training dataset is selected based on the corner case decision function.
Sampling-based Uncertainty Estimation for an Instance Segmentation Network
The examination of uncertainty in the predictions of machine learning (ML) models is receiving increasing attention.
Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving
Based on these predictions - and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users - the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e. g., moving from point A to B.
Description of Corner Cases in Automated Driving: Goals and Challenges
Scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous situations, termed corner cases (CC).
Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders
For this purpose, we propose a novel approach that allows for the generation of out-of-distribution datasets based on a given in-distribution dataset.
An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving
Systems and functions that rely on machine learning (ML) are the basis of highly automated driving.
Knowledge Representations in Technical Systems -- A Taxonomy
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.
