Effect of Explanatory Interfaces on Safety and Trust

Control transitions in Level 3 automated driving rely heavily on human-machine interfaces (HMIs), yet empirical insights into how explanatory HMIs (X-HMIs) shape driver trust, perceived safety, and takeover performance across pre-task, during-task, and post-task phases remain scarce. This driving simulator study (n = 64) employed a 2 (HMI type: basic B-HMI vs. explanatory X-HMI) × 3 (task phase) mixed design to compare objective takeover metrics (time, lateral stability) and subjective evaluations across planned and unplanned takeover scenarios. In terms of objective performance, X-HMI did not significantly reduce takeover time for either planned or unplanned takeovers (p = 0.295 and p = 0.340, respectively) , but markedly improved takeover quality: y-axis velocity deviation decreased by 46% (p < 0.001) and steering angle deviation by 58% (p < 0.001), indicating enhanced lateral stability and smoother control. Subjectively, X-HMI significantly elevated trust and perceived safety across all phases (all p < 0.001) with large effect sizes (Cohen’s d > 1.4). These findings suggest that X-HMIs enhance Level 3 automation safety and user experience not by expediting driver responses, but potentially by calibrating appropriate trust and contributing to stable control transitions. Phase-specific transparency—aligning explanatory content with the cognitive demands of pre-, during-, and post-task interactions—emerges as a core HMI design principle, providing a validated framework for developing user-centered automated driving systems.