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

53 citations · 107 across the 8 of their papers we have counts for

collaborators

8 papers

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…

cs.DC2020

Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance

Eduardo Sánchez Morales, Richard Membarth, Andreas Gaull +5

Due to the current developments towards autonomous driving and vehicle active safety, there is an increasing necessity for algorithms that are able to perform complex criticality p…

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…

cs.CV2020

Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles

Friedrich Kruber, Eduardo Sánchez Morales, Samarjit Chakraborty +1

The availability of real-world data is a key element for novel developments in the fields of automotive and traffic research. Aerial imagery has the major advantage of recording mu…

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…