Designing transparency for automated driving: Effects of ambient light cues and explanations on driver performance

Huang, Jingyu; Yinglin, Wu; Zhang, Tingru · 2025 · Crossref

DOI: 10.54941/ahfe1006871

archive: archived pipeline: cataloged verified

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Summary

This study addresses the challenge of designing transparency for autonomous vehicles (AVs) to improve human-machine collaboration and safety. While AVs offer significant benefits, their "black-box" nature creates uncertainty that hinders public acceptance. Existing transparency research often relies on foveal vision, which competes with driving tasks for attention. Guided by Situation Awareness-based Agent Transparency (SAT) theory, this research investigates whether conveying system uncertainty via peripheral ambient lighting and textual explanations can enhance driver performance and subjective experience without overloading cognitive resources. The researchers conducted a driving simulator experiment with 54 participants using a 3×2 between-subjects design. The independent variables were ambient light rhythm (constant, flashing, or breathing patterns indicating uncertainty levels) and the presence or absence of textual explanations during high-uncertainty scenarios. Participants navigated eight scenarios ranging from medium to extremely high uncertainty, such as driving in fog or rain. Data collection included eye-tracking (road fixation ratio), electrodermal activity (skin conductance), driving performance metrics (takeover reaction time, deceleration, lateral stability), and post-experiment questionnaires assessing workload, acceptance, and perceived usefulness. The results revealed a distinct trade-off between safety performance and subjective experience. Explanatory information significantly improved safety outcomes by reducing takeover reaction times and increasing the minimum time to collision, allowing drivers to anticipate risks and intervene earlier. However, explanations also impaired driving stability, as evidenced by a higher standard deviation in lateral position, and significantly reduced driver acceptance and perceived usefulness of the AV. Ambient lighting patterns did not significantly affect takeover performance metrics or road fixation ratios. Nevertheless, dynamic lighting (flashing and breathing) and explanations both increased physiological arousal, measured by higher skin conductance levels. Subjectively, dynamic light rhythms significantly increased driver workload compared to constant lighting, while explanations lowered user satisfaction metrics. The study concludes that transparency design involves balancing safety and user experience. If safety is the priority, breathing ambient lights combined with textual explanations are recommended to enhance risk perception and intervention efficiency. If user experience is paramount, constant ambient lighting is preferred to minimize workload and maintain higher acceptance levels. The findings validate the applicability of SAT theory in AV contexts and establish uncertainty visualization as a critical transparency construct, providing actionable guidance for manufacturers to optimize human-AV interaction.

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-09; verification: verified.

Topics

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• ehmi external hmi
• automation
• automation surprise
• situational awareness
• odd communication
• dark adaptation mesopic

Information type

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• Empirical Findings: behavioral performance data
• Theoretical Contribution: theory or model, conceptual framework