As vehicles become more advanced, in-car agents must manage increasingly complex interactions, heightening the need for effective information delivery. This paper investigates how different embodiments of in-car agents affect the delivery of various information types. We developed the ‘Drop-lit’ prototype to explore three embodiment features: physicality, characterization, and movement. In a user study with 20 participants, we compared three representative agent designs: abstraction, digital character, and mixed-media, across six categories of in-car information. Additionally, a co-design session allowed participants to self-customize and combine embodiment features for six specific driving scenarios. Results indicated that mixed-media agents were most effective for urgent warnings, digital characters for recommendations, and abstracted agents for simple reference information. The study also revealed how embodiment influenced experiential factors such as attention-grabbing, urgency, friendliness, trustworthiness, and playfulness, offering insights for optimizing agent design to enhance user engagement and information delivery in automotive contexts.
https://dl.acm.org/doi/10.1145/3706598.3713255
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)