11 citations · 12 across the 6 of their papers we have counts for
8 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…
Smart Device based Initial Movement Detection of Cyclists using Convolutional Neuronal Networks
Jan Schneegans, Maarten Bieshaar
For future traffic scenarios, we envision interconnected traffic participants, who exchange information about their current state, e.g., position, their predicted intentions, allow…
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…
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…
Where is my Device? - Detecting the Smart Device's Wearing Location in the Context of Active Safety for Vulnerable Road Users
Maarten Bieshaar
This article describes an approach to detect the wearing location of smart devices worn by pedestrians and cyclists. The detection, which is based solely on the sensors of the smar…