Beyond the Fear of the unknown: Designing a Transparency-Driven Interface for Calibrated Trust in Autonomous Vehicles

Abstract

As autonomous-driving systems take on increasing control authority, drivers are repositioned as human supervisors who must monitor decisions they did not make. Yet, today’s in-vehicle interfaces expose only the system’s final actions, leaving its underlying perception and reasoning invisible — a structural information asymmetry that manifests as a chronic fear of the unknown and undermines calibrated trust. Drawing on a formative study with 18 experienced ADAS users, we show that drivers’ trust is shaped more strongly by why and what-next explanations than by mere object recognition, and that the largest gap between current and desired information lies in intent, reasoning, confidence, and limits. Building on these findings, we derive three design principles that align the autonomous-driving pipeline with the driver’s reasoning order, and instantiate them in the Transparent ADAS Interface — a two-area design that communicates the vehicle’s behavior and intent alongside what the system perceives and how confidently it does so.