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20192021
most citedAn Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization

53 citations · 107 across the 10 of their papers we have counts for

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6 papers · 1 filter

eess.SP2020

High precision indoor positioning by means of LiDAR

Eduardo Sánchez Morales, Michael Botsch, Bertold Huber +1

The trend towards autonomous driving and the continuous research in the automotive area, like Advanced Driver Assistance Systems (ADAS), requires an accurate localization under all…

eess.SP2020

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…

eess.SP2020

Accuracy Characterization of the Vehicle State Estimation from Aerial Imagery

Eduardo Sánchez Morales, Friedrich Kruber, Michael Botsch +2

Due to their capability of acquiring aerial imagery, camera-equipped Unmanned Aerial Vehicles (UAVs) are very cost-effective tools for acquiring traffic information. However, not e…

eess.SP202041 cited

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…

eess.SP202053 cited

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…

eess.SP201913 cited

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…