Actions, Speech, and Looks: What Shapes How We Feel About In-Vehicle AI Assistants?

What should an intelligent in-vehicle assistant (IVA) look like, and how should it behave to truly enhance the in-car experience? We present a large-scale video-based online experiment (n = 1238) exploring how IVA design factors influence user perceptions. Participants evaluated two scenarios (adjusting temperature, adjusting seat position) across 32 conditions varying in autonomy (user-initiated, system-initiated, autonomous with explanation, autonom- ous without explanation), embodiment (abstract virtual agent, humanlike virtual agent, abstract robot, humanoid robot), and conversational style (formal, informal). Contrary to prevailing academic trends, our findings reveal a clear preference against robotic embodiments and high levels of autonomy, sometimes even when explainable. Instead, participants favored proactivity with lower system autonomy and less anthropomorphic designs. We discuss how these insights challenge current design assumptions and offer concrete guidelines for shaping IVAs that align with driver expectations and comfort. This work contributes an empirically grounded understanding of IVA appearance, behavior, and communication style to inform future human-centered automotive interaction design.