pascal-jansen / autovis

A mixed-immersive analysis tool for Automotive User Interfaces. Combines desktop and VR views for evaluating passenger behavior, physiology, and spatial interaction using avatars, trajectories, and heatmaps. Features context portals and driving-path events for automotive-specific visualizations. Validated with real-world data and case studies.

AutoVis: Enabling Mixed-Immersive Analysis of Automotive User Interface Interaction Studies

CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, https://doi.org/10.1145/3544548.3580760

Pascal Jansen, Julian Britten, Alexander Häusele, Thilo Segschneider, Mark Colley, and Enrico Rukzio

Automotive user interface (AUI) evaluation becomes increasingly complex due to novel interaction modalities, driving automation, heterogeneous data, and dynamic environmental contexts. Immersive analytics may enable efficient explorations of the resulting multilayered interplay between humans, vehicles, and the environment. However, no such tool exists for the automotive domain. With AutoVis, we address this gap by combining a non-immersive desktop with a virtual reality view enabling mixed-immersive analysis of AUIs. We identify design requirements based on an analysis of AUI research and domain expert interviews (N=5). AutoVis supports analyzing passenger behavior, physiology, spatial interaction, and events in a replicated study environment using avatars, trajectories, and heatmaps. We apply context portals and driving-path events as automotive-specific visualizations. To validate AutoVis against real-world analysis tasks, we implemented a prototype, conducted heuristic walkthroughs using authentic data from a case study and public datasets, and leveraged a real vehicle in the analysis process.

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Citation

If you use this code in your research, please cite our paper:

@inproceedings{jansen2024autovis,
author = {Jansen, Pascal and Britten, Julian and H\"{a}usele, Alexander and Segschneider, Thilo and Colley, Mark and Rukzio, Enrico},
title = {AutoVis: Enabling Mixed-Immersive Analysis of Automotive User Interface Interaction Studies},
year = {2023},
isbn = {9781450394215},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3544548.3580760},
doi = {10.1145/3544548.3580760},
booktitle = {Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems},
articleno = {378},
numpages = {23},
keywords = {Immersive analytics, automotive user interfaces, interaction analysis, virtual reality, visualization},
location = {Hamburg, Germany},
series = {CHI '23}
}