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

Abstract

Autonomous vehicle (AV) demonstrates significant potential to reduce traffic accidents, lower harmful emissions, and enhance mobility, potentially transforming traditional road transportation. However, AV based on artificial intelligence algorithms exhibits characteristics such as black-box nature, uncertainty, and autonomy, posing challenges including low public acceptance and difficulties in human-machine collaboration. In this context, enhancing transparency is crucial for promoting AV adoption, optimizing the human-AV co-driving experience, and improving driving safety. This study employs the Situation awareness-based Agent Transparency (SAT) theory, treating the system’s level of uncertainty in complex driving scenarios as transparency information conveyed via ambient lighting. Driving simulator experiments were conducted, manipulating light pattern (constant, blinking, breathing) and explanatory information provision (presence, absence) as independent variables. Data from 54 participants—including eye tracking, skin conductance, questionnaires, and takeover performance—were collected and analysed.Results indicate that both breathing ambient lighting and explanatory information effectively enhance drivers’ perception of potential risks, with explanatory information significantly reducing takeover reaction time. However, compared to constant lighting, both blinking and breathing patterns substantially increase driver workload, while explanatory information impairs driving stability and reduces driver acceptance and perceived usefulness of AV. This research expands transparency literature, validates the applicability of SAT theory in autonomous driving contexts, quantifies the trade-off between safety and subjective experience, and establishes uncertainty visualization as a distinct transparency construct. It provides a theoretical foundation for improving human subjective driving experiences with AV2. Also, the findings offer actionable guidance for AV transparency design, assisting manufacturers in determining how to design and present transparency information to enhance user safety during automated driving.

Keywords: Autonomous Driving, Transparency, Human-AV Collaboration, Uncertainty, Explanation, Information Presentation, Ambient Light

DOI: 10.54941/ahfe1006871