Evaluating In-Vehicle Drunk-Driving Warning Symbols Using Generative AI: A Comparison with Human Judgments

This study examined whether Generative Artificial Intelligence (AI) can evaluate in-vehicle drunk-driving warning symbols (telltales) in ways comparable to human perceptual and semantic judgments. Using visibility and intuitiveness ratings collected from Korean and Texas-based U.S. participants, the same 30 telltales were evaluated by GPT-4o and GPT-5.5. Pearson correlation and agreement analyses revealed significant positive associations between AI and human evaluations for both metrics, with stronger correspondence in intuitiveness. Symbols with clear meanings, simple structures, and familiar alcohol-, vehicle-, or prohibition-related cues received consistently favorable ratings from both AI and humans. These results suggest that AI judgments may be associated with symbolic meaning as well as visual form, supporting its potential as a preliminary evaluation tool. In early design stages of safety-critical domains such as drunk-driving prevention, AI may help prescreen visual designs, improve evaluation efficiency, strengthen preliminary design review, and support human-centered telltale development for practical vehicle interfaces.