Drivers' Age and Automated Vehicle Explanations

Zhang, Qiaoning · 2021 · Sustainability

DOI: 10.3390/su13041948

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Summary

This study investigates how driver age influences the effectiveness of automated vehicle (AV) explanations in fostering trust and reducing anxiety and cognitive effort. While AVs offer significant societal benefits, particularly for older adults who may outlive their ability to drive safely, lack of trust remains a primary barrier to adoption. Although providing explanations about AV decision-making can reduce uncertainty, it may also increase cognitive load. Given that cognitive abilities and comfort with automation vary across age groups, the authors sought to determine whether explanations are equally beneficial for younger, middle-aged, and older drivers. The researchers conducted a mixed-design experiment with 40 participants divided into three age groups: younger (18–24 years), middle-aged (25–54 years), and older (55+ years). Participants used a high-fidelity driving simulator programmed to simulate SAE Level 4 automation. The study employed a 4 × 3 design, exposing participants to four AV explanation conditions: no explanation, explanation provided before the AV took action, explanation provided after the action, and explanation accompanied by a request for permission to act. Each condition involved three unexpected events (e.g., swerving vehicles, police approaches, reroutes) across urban, highway, and rural environments. Trust was measured using a validated seven-point Likert scale assessing competence, predictability, and reliability, while anxiety was measured using adjective-based ratings. The results demonstrated that driver age significantly moderates the impact of AV explanations. For all age groups, explanations provided before the AV took action yielded the highest trust and lowest cognitive effort. However, the request-for-permission condition produced the highest trust and lowest effort only for older drivers, suggesting this group benefits from retaining a sense of control. In contrast, younger drivers experienced the lowest anxiety and effort when explanations were given after the action; notably, this post-action condition generated the highest anxiety for middle-aged drivers and the highest effort for older drivers. These findings indicate that a single explanation strategy is not optimal for all users. The study concludes that age-based biases in AI system design must be addressed to promote inclusive AV adoption. The findings imply that AV interfaces should be adaptable, potentially offering pre-action explanations as a default for general trust-building while incorporating permission-seeking mechanisms for older drivers to mitigate anxiety and enhance perceived control. By tailoring explanation timing and autonomy levels to specific age groups, designers can better align AV systems with the cognitive and psychological needs of diverse user populations, thereby facilitating broader acceptance and safety.

Key finding

Providing explanations before an automated vehicle takes action maximizes trust and minimizes effort for all age groups, whereas request-for-permission explanations are uniquely beneficial for older drivers.

Methodology

simulator

Sample size: 40

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• trust calibration
• acceptance adoption
• automation surprise
• automation

Information type

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• Empirical Findings: self report data
• Theoretical Contribution: theory or model, computational model