Longitudinal Effects of Visualizing Uncertainty of Situation Detection and Prediction of Automated Vehicles on User Perceptions
Journal Paper at Transportation Research Part F, doi: 10.1016/j.trf.2025.05.013
Pascal Jansen*, Mark Colley*, Max Rädler*, Jonas Schwedler, Enrico Rukzio *Joint First Authors
This repository contains the source code for the longitudinal study website. The website hosted six videos of day/night real-world driving scenes that the participants watched over three days.
The data inside the "database" folder is a sample and does not represent the study result dataset. We will make the data available upon acceptance.
- Computer vision models visualized automated vehicles' detection, prediction, and planning.
- Longitudinal study investigating the impact of visualized uncertainties.
- Three morning and evening sessions regarding user trust, perceived safety, and cognitive load.
- Trust and perceived safety increased over time, evening higher than morning.
- Mixed reactions to inconsistencies in pedestrian detection and intention prediction.
@article{JANSEN2025536,
title = {Longitudinal effects of visualizing uncertainty of situation detection and prediction of automated vehicles on user perceptions},
journal = {Transportation Research Part F: Traffic Psychology and Behaviour},
volume = {113},
pages = {536-553},
year = {2025},
issn = {1369-8478},
doi = {https://doi.org/10.1016/j.trf.2025.05.013},
url = {https://www.sciencedirect.com/science/article/pii/S1369847825001779},
author = {Pascal Jansen and Mark Colley and Max Rädler and Jonas Schwedler and Enrico Rukzio},
keywords = {Automated vehicles, Automotive, Explainable, Artificial intelligence, Longitudinal user study},
abstract = {This paper explores the impact of uncertainty visualizations in automated vehicle (AV) functionality on user perceptions over a three-day longitudinal study. Participants (N=50) watched real-world driving videos twice daily, in the morning and evening. These videos depicted morning and evening commutes, featuring visualizations of AVs' pedestrian detection, vehicle recognition, and pedestrian intention prediction. We measured perceived safety, trust, mental workload, and cognitive load using a within-subjects design. Results show increased perceived safety and trust over time, with higher ratings in the evening sessions, reflecting greater predictability and user confidence in AV by the study's end. However, inconsistencies in pedestrian detection and intention prediction led to mixed reactions, highlighting the need for visualization stability and clarity refinement. Participants also desired a feature indicating the AV's intended path and options for manual intervention. Our findings suggest transparency and usability in AV visualizations can foster trust and perceived safety, informing future AV interface design.}
}