As vehicles progress toward higher levels of autonomy, human–vehicle interaction will increasingly shift from driving-related tasks toward conversational engagement, highlighting the importance of conversational agent design. This paper presents a multi-method investigation of user-centered and brand-consistent visualization strategies for in-car conversational agents (ICAs), combining a quantitative survey based on personality and brand frameworks, an exploratory benchmarking analysis of existing automotive conversational agents, and an experimental study in a simulated driving environment examining responses to varying levels of visual abstraction and anthropomorphism. Preliminary results suggest that functional reliability and voice interaction quality form the primary acceptance baseline, while visual representation serves as a secondary and strategically differentiating layer. Rather than highly anthropomorphic or complex visualizations, adaptive concepts offering adjustable expressiveness, personalization, and modular abstraction emerge as a robust design direction for integrating ICAs into vehicle identity, user experience, and long-term human–machine relationships.
Esther Carolina Kaehne, Ignacio J. Alvarez· Adjunct Proceedings of the 1...· 0 citations
This Work-in-Progress examines whether personality-informed prompting changes perceived LLM emotional support in driving scenarios designed to elicit stress. A condition-order-balanced, within-subject CARLA simulator study (n = 14) compared a baseline with a Driver Personality Profile (DPP) condition; a supplementary online video pilot (n = 12) examined response perception without driving control or live-system latency. No statistically detectable condition differences emerged for usefulness, ease of use, privacy concerns, or social/emotional presence, and only 7 of 14 simulator participants identified the DPP condition correctly. A post-hoc lexical audit showed that both conditions frequently reused generic supportive scaffolding. We discuss output anchoring as one tentative interpretation, not an established phenomenon: the evidence cannot distinguish constraint-dominated generation from a weak personalization manipulation or limitations of the 7B model. The findings motivate stronger, independently validated personalization manipulations and privacy-aware in-vehicle support.
Max Mittelstädt, Ece Sutanrikulu, Lumbardh Ljatifi et al.· Adjunct Proceedings of the 1...· 0 citations
Overall, multimodal explanations appear beneficial, particularly during initial use, but should adapt to user needs and context, but should adapt to user needs and context.
Ignacio J. Alvarez, Tobias Seidl· Adjunct Proceedings of the 1...· 0 citations
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