Exploring the Design Space of Adaptive Explanations in Automated Vehicles

Abstract

Explanations in automated vehicles have been proposed to support users’ understanding, trust, and acceptance of automated driving behavior. However, many explanation interfaces remain static interventions, even though users’ information needs may vary depending on driving context, familiarity, perceived criticality, and preference. This work presents a simulator platform for prototyping adaptive in-vehicle explanations in automated driving. The platform combines a Unity-based driving environment, a motion-based, VR-enabled setup, scripted traffic incidents, an in-vehicle explanation interface, and a feedback loop to adapt explanations. The system uses a large language model-based explanation pipeline that receives scenario information and user feedback to determine whether an explanation should be presented, how detailed it should be, and how it should be delivered. We describe the platform, outline the design space it enables, and reflect on how such systems can support AutomotiveUI research on adaptive explanations, explanation suppression, user control, and human-AI interaction in automated vehicles.