Search Results for author:
Found 11 papers, 3 papers with code
Considering Human Factors in Risk Maps for Robust and Foresighted Driver Warning
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.
Probabilistic Uncertainty-Aware Risk Spot Detector for Naturalistic Driving
Risk assessment is a central element for the development and validation of Autonomous Vehicles (AV).
Optimization of Velocity Ramps with Survival Analysis for Intersection Merge-Ins
We consider the problem of correct motion planning for T-intersection merge-ins of arbitrary geometry and vehicle density.
Importance Filtering with Risk Models for Complex Driving Situations
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.
Relate to Predict: Towards Task-Independent Knowledge Representations for Reinforcement Learning
For this, we introduce a semantic module that predicts an objects' semantic state based on its context.
The Set Autoencoder: Unsupervised Representation Learning for Sets
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.
Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial Optimization
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled.
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial Optimization
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled.
Training a Restricted Boltzmann Machine for Classification by Labeling Model Samples
We propose an alternative method for training a classification model.
Denoising Autoencoders for fast Combinatorial Black Box Optimization
The number of fitness evaluations is higher than for BOA, but competitive with RBM-EDA.
Scalability of using Restricted Boltzmann Machines for Combinatorial Optimization
The results are compared to the Bayesian Optimization Algorithm, a state-of-the-art EDA.
