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.

While speculative methods (e.g., design fiction) could help industry to reflect critically about future products and services, little is known about their potentials and limits when applied in a corporate setting. In this case, we worked with an automobile manufacturer to explore the near future of self-driving cars. For more than a year, we speculated using diverse methods, developed stories about a future with self-driving cars, and told them through 24 artifacts (e.g., city map, quick start guides). In this paper, we scrutinize the process, discuss what has worked and what not, which challenges we faced, and how corporate requirements shaped the emerging design fiction. We saw a trend toward affirmative rather than speculative design, driven by product-centricity and the need to justify process and outcome through concrete innovation. While speculation raised interesting questions about technological futures, it remained a challenge to transform them into concrete design implications.

In a fully autonomous driving situation, passengers hand over the steering control to a highly automated system. Autonomous driving behaviour may lead to confusion and negative user experience. When establishing such new technology, the user’s acceptance and understanding are crucial factors regarding success and failure. Using a driving simulator and a mobile application, we evaluated if system transparency during and after the interaction can increase the user experience and subjective feeling of safety and control. We contribute an initial guideline for autonomous driving experience design, bringing together the areas of user experience, explainable artificial intelligence and autonomous driving. The AVAM questionnaire, UEQ-S and interviews show that explanations during or after the ride help turn a negative user experience into a neutral one, which might be due to the increased feeling of control. However, we did not detect an effect for combining explanations during and after the ride.