Presentation
A Driver-In-The-Loop Study of Human-AI Joint Replanning
SessionPoster Session 1
DescriptionThis study investigates human-AI teaming requirements in the context of ill-structured replanning tasks, where evolving goals, ambiguous constraints, and shifting environmental states create coordination demands with AI agents. We conducted a high-fidelity staged world experiment using a driver-in-the-loop simulation, wherein participants completed two event-driven driving scenarios using one of two AI assistant architectures. These prototypes embodied distinct functionality and coordination strategies grounded in a Flexecution model of replanning. Our mixed-method analysis draws from observations and qualitative interviews to evaluate how participants navigated the trade space between the cognitive benefits of AI support and the coordination overhead of engaging with the system. Results reveal how contextual control modes modulate interaction strategies and suggest design implications for AI teammates that better support human adaptation under varying workload conditions and temporal constraints.
Event Type
Poster
TimeTuesday, October 14th5:30pm - 6:30pm CDT
LocationRiverside East