24 citations · 32 across the 9 of their papers we have counts for
9 papers
Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information
Stefan Zernetsch, Oliver Trupp, Viktor Kress +2
This article presents a novel approach to incorporate visual cues from video-data from a wide-angle stereo camera system mounted at an urban intersection into the forecast of cycli…
Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users' Trajectories
Viktor Kress, Fabian Jeske, Stefan Zernetsch +2
In this article, an approach for probabilistic trajectory forecasting of vulnerable road users (VRUs) is presented, which considers past movements and the surrounding scene. Past m…
Cyclist Intention Detection: A Probabilistic Approach
Stefan Zernetsch, Hannes Reichert, Viktor Kress +2
This article presents a holistic approach for probabilistic cyclist intention detection. A basic movement detection based on motion history images (MHI) and a residual convolutiona…
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…
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…
Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network
Stefan Zernetsch, Viktor Kress, Bernhard Sick +1
In this article, we present a novel approach to detect starting motions of cyclists in real world traffic scenarios based on Motion History Images (MHIs). The method uses a deep Co…
Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble
Maarten Bieshaar, Stefan Zernetsch, Andreas Hubert +2
In future, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation on different levels, such as situatio…
Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure
Günther Reitberger, Stefan Zernetsch, Maarten Bieshaar +3
In future traffic scenarios, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation based on data or in…