no code implementations • 6 Jun 2023 • Tim Puphal, Ryohei Hirano, Malte Probst, Raphael Wenzel, Akihito Kimata
In this paper, we therefore propose a warning system that uses human states in the form of driver errors and can warn users in some cases of upcoming risks several seconds earlier than the state of the art systems not considering human factors.
no code implementations • 13 Mar 2023 • Tim Puphal, Malte Probst, Julian Eggert
Risk assessment is a central element for the development and validation of Autonomous Vehicles (AV).
no code implementations • 13 Mar 2023 • Tim Puphal, Malte Probst, Yiyang Li, Yosuke Sakamoto, Julian Eggert
We consider the problem of correct motion planning for T-intersection merge-ins of arbitrary geometry and vehicle density.
no code implementations • 13 Mar 2023 • Tim Puphal, Raphael Wenzel, Benedict Flade, Malte Probst, Julian Eggert
Based on the results, we can further derive a novel filter architecture with multiple filter steps, for which risk models are recommended for each step, to further improve the robustness.
no code implementations • 10 Dec 2022 • Thomas Schnürer, Malte Probst, Horst-Michael Gross
For this, we introduce a semantic module that predicts an objects' semantic state based on its context.
no code implementations • ICLR 2018 • Malte Probst
It is closely related to sequence-to-sequence models, which learn fixed-sized latent representations for sequences, and have been applied to a number of challenging supervised sequence tasks such as machine translation, as well as unsupervised representation learning for sequences.
1 code implementation • 30 Sep 2015 • Malte Probst
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled.
1 code implementation • 22 Sep 2015 • Malte Probst, Franz Rothlauf
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled.
no code implementations • 3 Sep 2015 • Malte Probst, Franz Rothlauf
We propose an alternative method for training a classification model.
1 code implementation • 6 Mar 2015 • Malte Probst
The number of fitness evaluations is higher than for BOA, but competitive with RBM-EDA.
no code implementations • 27 Nov 2014 • Malte Probst, Franz Rothlauf, Jörn Grahl
The results are compared to the Bayesian Optimization Algorithm, a state-of-the-art EDA.