Sim-DSE: Mediating Multi-User Automations in Cars through Simulation-Augmented Decision Space Exploration

In-vehicle interaction is often envisioned as requiring as little explicit user input as possible. In multi-user scenarios, however, varying user states and expectations can lead to conflicting automation scenarios. Including more than one passenger introduces complexity to the interaction space that has been neglected by existing literature. We tackle this with a systematic Simulation-Augmented Decision Space Exploration (Sim-DSE) approach: Relevant in-vehicle interactions are identified in an expert workshop (N = 5) and utilized to model a suitable decision space for in-car automations. We then simulate 1000 permutations in this decision space using an agentic AI framework to derive the reasoning behind these automations. Next, we validate the simulated outcomes and their rationales through human feedback in an online survey (N = 100) and distill the results into design guidelines for multi-user automation. The method and guidelines help future researchers and developers to mitigate automation conflicts in multi-user scenarios.