53 citations · 107 across the 4 of their papers we have counts for
3 papers · 1 filter
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…
Highway traffic data: macroscopic, microscopic and criticality analysis for capturing relevant traffic scenarios and traffic modeling based on the highD data set
Friedrich Kruber, Jonas Wurst, Samarjit Chakraborty +1
This work provides a comprehensive analysis on naturalistic driving behavior for highways based on the highD data set. Two thematic fields are considered. First, some macroscopic a…