41 citations · 135 across the 21 of their papers we have counts for
9 papers · 1 filter
Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence
Maarten Bieshaar, Günther Reitberger, Stefan Zernetsch +3
Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play an important role in future traffic. To avoid accidents and achieve a highly efficient traffic flow, it is imp…
Coopetitive Soft Gating Ensemble
Stephan Deist, Maarten Bieshaar, Jens Schreiber +2
In this article, we propose the Coopetititve Soft Gating Ensemble or CSGE for general machine learning tasks and interwoven systems. The goal of machine learning is to create model…
Starting Movement Detection of Cyclists Using Smart Devices
Maarten Bieshaar, Malte Depping, Jan Schneegans +1
In near future, vulnerable road users (VRUs) such as cyclists and pedestrians will be equipped with smart devices and wearables which are capable to communicate with intelligent ve…
A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation
André Gensler, Bernhard Sick
In this article, we propose a novel ensemble technique with a multi-scheme weighting based on a technique called coopetitive soft gating. This technique combines both, ensemble mem…
Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning
Michael Goldhammer, Sebastian Köhler, Stefan Zernetsch +3
Avoiding collisions with vulnerable road users (VRUs) using sensor-based early recognition of critical situations is one of the manifold opportunities provided by the current devel…
Highly Automated Learning for Improved Active Safety of Vulnerable Road Users
Maarten Bieshaar, Günther Reitberger, Viktor Kreß +4
Highly automated driving requires precise models of traffic participants. Many state of the art models are currently based on machine learning techniques. Among others, the require…