Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Bieshaar, Maarten"'
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 passengers safely
Externí odkaz:
http://arxiv.org/abs/2404.11266
In safety-critical domains like automated driving (AD), errors by the object detector may endanger pedestrians and other vulnerable road users (VRU). As common evaluation metrics are not an adequate safety indicator, recent works employ approaches to
Externí odkaz:
http://arxiv.org/abs/2402.02986
Autor:
Rösch, Kevin, Heidecker, Florian, Truetsch, Julian, Kowol, Kamil, Schicktanz, Clemens, Bieshaar, Maarten, Sick, Bernhard, Stiller, Christoph
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 information suc
Externí odkaz:
http://arxiv.org/abs/2210.08885
Autor:
Bogdoll, Daniel, Breitenstein, Jasmin, Heidecker, Florian, Bieshaar, Maarten, Sick, Bernhard, Fingscheidt, Tim, Zöllner, J. Marius
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 essential p
Externí odkaz:
http://arxiv.org/abs/2109.09607
Autor:
Möller, Felix, Botache, Diego, Huseljic, Denis, Heidecker, Florian, Bieshaar, Maarten, Sick, Bernhard
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 based on a give
Externí odkaz:
http://arxiv.org/abs/2105.02965
Autor:
Heidecker, Florian, Breitenstein, Jasmin, Rösch, Kevin, Löhdefink, Jonas, Bieshaar, Maarten, Stiller, Christoph, Fingscheidt, Tim, Sick, Bernhard
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 of those sit
Externí odkaz:
http://arxiv.org/abs/2103.03678
In this article, we present a novel approach to multivariate probabilistic forecasting. Our approach is based on an extension of single-output quantile regression (QR) to multivariate-targets, called quantile surfaces (QS). QS uses a simple yet compe
Externí odkaz:
http://arxiv.org/abs/2010.05898
This article is about an extension of a recent ensemble method called Coopetitive Soft Gating Ensemble (CSGE) and its application on power forecasting as well as motion primitive forecasting of cyclists. The CSGE has been used successfully in the fie
Externí odkaz:
http://arxiv.org/abs/2004.14026
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 knowledge
Externí odkaz:
http://arxiv.org/abs/2001.04835
Fusion is a common tool for the analysis and utilization of available datasets and so an essential part of data mining and machine learning processes. However, a clear definition of the type of fusion is not always provided due to inconsistent litera
Externí odkaz:
http://arxiv.org/abs/2001.04171