  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.
