BlurDriving: Investigating How Personalized Blur Techniques Impact Drivers’ Performance in Virtual Reality

Yuan Li, Mark Colley, Xinyue Gui, Cristian Rendon-Cardona, Pascal Jansen, Christian Sandor, Takeo Igarashi
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT / UbiComp 2026) · Journal article
Accepted with minor revision · To appear
BibTeX Paper to appear
BlurDriving VR scene with personalized blur targets

BlurDriving explores how personalized blur techniques can modulate visual information in VR driving scenes and how such techniques impact driver performance and attention.

Abstract

Virtual reality driving environments are increasingly used to study driver behavior, perception, and interaction with complex road scenes. BlurDriving investigates how personalized blur techniques affect drivers’ performance and visual attention by selectively modulating scene elements such as road cars, pedestrians, buildings, and automated-driving-related objects. Through this work, we examine how visual abstraction can shape attention allocation and driving behavior in simulated traffic environments.

BibTeX

@article{li2026blurdriving,
  title={BlurDriving: Investigating How Personalized Blur Techniques Impact Drivers' Performance in Virtual Reality},
  author={Li, Yuan and Colley, Mark and Gui, Xinyue and Rendon-Cardona, Cristian and Jansen, Pascal and Sandor, Christian and Igarashi, Takeo},
  journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  year={2026},
  note={Accepted with minor revision, to appear}
}