Stefan Zernetsch

Orcid: 0000-0003-2016-5059

According to our database1, Stefan Zernetsch authored at least 26 papers between 2013 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users' Trajectories.
IEEE Trans. Intell. Veh., March, 2023

2022
Maschinelle Lernverfahren zur videobasierten Intentionserkennung von Radfahrern mit stationären Kameras.
PhD thesis, 2022

A Holistic View on Probabilistic Trajectory Forecasting - Case Study. Cyclist Intention Detection.
Proceedings of the 2022 IEEE Intelligent Vehicles Symposium, 2022

2021
Cyclist Intention Detection: A Probabilistic Approach.
CoRR, 2021

Cyclist Motion State Forecasting - Going beyond Detection.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning A Case Study: Overtaking Cyclists.
Proceedings of the IEEE Intelligent Vehicles Symposium Workshops, 2021

Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information.
Proceedings of the IEEE International Smart Cities Conference, 2021

2020
Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning.
IEEE Trans. Intell. Transp. Syst., 2020

Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks.
Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020

Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution.
Proceedings of the 25th International Conference on Pattern Recognition, 2020

Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks.
Proceedings of the Pattern Recognition. ICPR International Workshops and Challenges, 2020

2019
Pose Based Trajectory Forecast of Vulnerable Road Users.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2019

Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks.
Proceedings of the 2019 IEEE Intelligent Vehicles Symposium, 2019

Pose Based Start Intention Detection of Cyclists.
Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, 2019

Start Intention Detection of Cyclists using an LSTM Network.
Proceedings of the 49. Jahrestagung der Gesellschaft für Informatik, 50 Jahre Gesellschaft für Informatik - Informatik für Gesellschaft, INFORMATIK 2019, 2019

2018
Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble.
IEEE Trans. Intell. Veh., 2018

Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence.
CoRR, 2018

Highly Automated Learning for Improved Active Safety of Vulnerable Road Users.
CoRR, 2018

Human Pose Estimation in Real Traffic Scenes.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2018

Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network.
Proceedings of the 2018 IEEE Intelligent Vehicles Symposium, 2018

Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure.
Proceedings of the 21st International Conference on Intelligent Transportation Systems, 2018

2017
Model-predictive planning for autonomous vehicles anticipating intentions of vulnerable road users by artificial neural networks.
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Cyclists' starting behavior at intersections.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2017

Cooperative starting intention detection of cyclists based on smart devices and infrastructure.
Proceedings of the 20th IEEE International Conference on Intelligent Transportation Systems, 2017

2016
Trajectory prediction of cyclists using a physical model and an artificial neural network.
Proceedings of the 2016 IEEE Intelligent Vehicles Symposium, 2016

2013
Early prediction of a pedestrian's trajectory at intersections.
Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems, 2013


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