Seeing the automation malfunction before it happens: designing information cues that support driver situation awareness

OBJECTIVE: Advanced Driver Assistance Systems (ADAS) may degrade in complex environments, producing malfunction states that require driver readiness to intervene. However, empirical guidance remains limited on how vehicle-driver communication should be structured to support situation awareness (SA) during such events. METHODS: This study evaluates three structured information cue functions, Compromised Function (CF), Tracking Status (TS), and Action Rationale (AR), designed to support perception (Occurrence), comprehension (Cause), and projection (Solution) during ADAS malfunctions. Twenty-seven ADAS-equipped drivers evaluated cue effectiveness across three representative malfunction scenarios using a mixed experimental design. Linear mixed-effects models assessed absolute effectiveness, relative differences among cue types, and demographic moderation. RESULTS: CF demonstrated the most stable and robust advantage at lower SA levels. At the Occurrence stage, CF was rated significantly higher than TS (Δ = 1.33) and AR (Δ = 0.93). A similar pattern emerged at the Cause stage, where CF exceeded AR (Δ = 0.73) and TS (Δ = 0.72). Demographic moderation effects, including age, gender, education, and ADAS experience, were consistently observed, indicating stable differences in cue effectiveness across user groups. CONCLUSIONS: These findings highlight the importance of clearly communicating system vulnerabilities to support early malfunction recognition and to guide more effective transparency design for drivers facing automation malfunctions.