162 citations
- Freie Universität BerlinDE10 papers
- Friedrich-Alexander-Universität Erlangen-NürnbergDE9 papers
- University of Veterinary Medicine ViennaAT9 papers
- Technical University of MunichDE8 papers
- Flensburg University of Applied SciencesDE7 papers
- The Schwarzman Animal Medical CenterUS5 papers
- Medical University of ViennaAT4 papers
- Europa-Universität FlensburgDE3 papers
- IDEXX Laboratories (Germany)DE3 papers
- Michigan State UniversityUS3 papers
- Ross University School of Veterinary MedicineKN3 papers
- Universitätsklinikum ErlangenDE3 papers
7 papers · 1 filter
InTAS -- The Ingolstadt Traffic Scenario for SUMO
Silas C. Lobo, Stefan Neumeier, Evelio M. G. Fernandez +1
Vehicular Ad Hoc Networks (VANETs) are expected to be the next big step towards safer road transport, supporting applications to exchange information between vehicles. To develop n…
Artery-C -- An OMNeT++ Based Discrete Event Simulation Framework for Cellular V2X
Anupama Hegde, Andreas Festag
Cellular Vehicle-to-X (Cellular V2X) is a communication technology that aims to facilitate the communication among vehicles and with the roadside infrastructure. Introduced with LT…
High Precision Indoor Navigation for Autonomous Vehicles
Eduardo Sánchez Morales, Michael Botsch, Bertold Huber +1
Autonomous driving is an important trend of the automotive industry. The continuous research towards this goal requires a precise reference vehicle state estimation under all circu…
An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images
Jonas Wurst, Alberto Flores Fernández, Michael Botsch +1
A novel unsupervised outlier score, which can be embedded into graph based dimensionality reduction techniques, is presented in this work. The score uses the directed nearest neigh…
Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification
Friedrich Kruber, Jonas Wurst, Eduardo Sánchez Morales +2
The goal of this paper is to provide a method, which is able to find categories of traffic scenarios automatically. The architecture consists of three main components: A microscopi…
An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization
Friedrich Kruber, Jonas Wurst, Michael Botsch
A modification of the Random Forest algorithm for the categorization of traffic situations is introduced in this paper. The procedure yields an unsupervised machine learning method…