Main document
Virtual Cycling Environment (VCE)

Virtual Cycling Environment for interactive bicycling experiments with vehicle-to-anything (V2X) communication
Summary
In order to trace and record realistic and reliable cyclist behavior, we developed the Virtual Cycling Environment (VCE). It allows cyclists to ride a virtual bicycle in a 3D virtual reality environment by interacting with a physical bicycle on a training stand. Foreign traffic (i.e., cars) and wireless networking are provided by the specialized simulators SUMO and Veins, respectively. The physical bike simulator is then coupled via the Ego-Vehicle Interface (EVI) to this simulation platform. The VCE provides a high degree of realism to the cyclist, thanks to the haptics of a physical bicycle combined with virtual reality systems. Researchers can leverage this to study the interaction of cyclists and their traffic environment without the danger of physical harm. Thanks to the coupling to Veins, even future assistance systems relying on communication can be tested.
If you are using components (or the concept) of the VCE or traces we recorded with the VCE, we would appreciate a citation:Julian Heinovski, Lukas Stratmann, Dominik S. Buse, Florian Klingler, Mario Franke, Marie-Christin H. Oczko, Christoph Sommer, Ingrid Scharlau and Falko Dressler, "Modeling Cycling Behavior to Improve Bicyclists' Safety at Intersections – A Networking Perspective," Proceedings of 20th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2019), Washington, D.C., June 2019. [DOI, BibTeX, PDF and Details...]
Sources
- Documentation
- GitHub project
- VCE Bicycle Traces 2019 (zip) [index, md5]
- VCE Bicycle Traces 2019 (tar.bz2) [index, md5]
Press & Media
- Article @ HNI aktuell 01/2019 (14.05.2019)
- Virtual Cycling Enviroment @ YouTube
- Virtual Cycling Enviroment - Intersection Collision Warning via V2X @ YouTube
See also
- Project Virtual Cycling Environment (VCE)
- Project Safety4Bikes
Contact
Extras
Featured Paper
- Explainability of Neural Networks for Symbol Detection in Molecular
Communication Channels
Recent research in molecular communication (MC) suggests machine learning (ML) models for symbol detection, avoiding the unfeasibility of end-to-end channel models. Ho...
News
- November 15, 2023
Keynote at IEEE LATINCOM 2023
Falko Dressler gave a keynote titled 6G Virtualized Edge ... - November 03, 2023
Talk at KAIST Seminar on Mobile & Wireless in EE
Falko Dressler gave a seminar talk titled Virtualized Edg... - October 12, 2023
Paper presentation at IEEE VTC 2023-Fall
Atefeh Rezaei presented our paper titled Resource Allocat... - October 03, 2023
Paper presentation at European Wireless 2023
Agon Memedi presented our paper titled One-Class Support ... - October 02, 2023
Poster presentation at ACM MobiCom 2023
Jonas Kuß presented our paper titled A Measurement Syste...