Measuring Visual Clutter in AI-Generated Pillar-to-Pillar Automotive Displays: Objective Metrics, Perception, and Search Performance

Large pillar-to-pillar (P2P) displays are replacing traditional instrument clusters, but their visual richness may impair visual search and increase driver distraction. We examined how visual properties of large automotive displays relate to perceived clutter and search performance using subjective, objective, and behavioral measures. Fifty-five participants viewed 18 AI-generated dashboard videos, completed a detection task, and rated clutter, density, overlap, colorfulness, and brightness; corresponding objective metrics were computed from video frames. Subjective density and overlap best predicted clutter, while overlap, edge density, and colorfulness predicted search time. Factor analysis revealed two dimensions, spatial complexity and surface appearance, showing that spatial properties primarily drive clutter perception and that objective metrics can support early-stage interface screening.