Look who's talking now: Implications of AV's explanations on driver's trust, AV preference, anxiety and mental workload

Haspiel, Jacob · 2019 · Transportation Research Part C: Emerging Technologies

DOI: 10.1016/j.trc.2019.05.025

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Summary

This study investigates how automated vehicle (AV) explanations influence driver trust, AV preference, anxiety, and mental workload, addressing a critical barrier to AV adoption. While explanations are known to promote automation use, their specific impact on AV acceptance remains unclear, particularly regarding the timing of explanations and the degree of autonomy. Guided by Uncertainty Reduction Theory, the researchers hypothesized that explanations provided before an AV acts would yield better outcomes than those provided after, and that allowing drivers to approve or disapprove actions would further enhance trust and reduce anxiety. The researchers conducted a within-subject experiment using a high-fidelity driving simulator with 32 participants. The study manipulated two factors: explanation timing (before vs. after action) and degree of autonomy (automatic execution vs. requiring driver permission). Participants experienced four conditions: (1) no explanation, (2) explanation provided seven seconds before the AV acted, (3) explanation provided within one second after the action, and (4) explanation followed by a request for driver approval. Each condition involved a 6–8 minute drive containing three unexpected events, such as swerving vehicles or police presence. Subjective measures for trust, preference, anxiety, and mental workload were collected after each drive using validated scales, including the NASA-TLX for workload. The results indicated that explanations provided before the AV acted were associated with significantly higher trust in and preference for the AV compared to no explanation or post-action explanations. However, there were no significant differences in anxiety or mental workload across the conditions. The study found that while pre-action explanations effectively boosted positive attitudes, they did not alleviate driver anxiety or reduce cognitive load. Additionally, the condition requiring driver permission did not yield the hypothesized superior outcomes across all measures, suggesting that the mere presence of a veto option does not automatically optimize driver response. These findings have significant implications for AV interface design and adoption strategies. The study demonstrates that the timing of explanations is a critical factor; providing transparency before an action occurs is more effective for building trust and preference than explaining actions after they happen. However, the lack of impact on anxiety and workload suggests that explanations alone may not be sufficient to address all psychological barriers to AV use. Designers must consider that while pre-action explanations foster acceptance, additional interventions may be required to mitigate driver anxiety and manage mental workload during automated driving.

Key finding

Explanations delivered before the AV acted produced significantly higher driver trust and AV preference than no explanation, after-action explanation, or a permission-required condition; anxiety and NASA-TLX mental workload did not differ across conditions (N=32, within-subjects simulator study).

Methodology

simulator

Sample size: 32

Provenance

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Topics

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• trust calibration
• acceptance adoption
• automation surprise
• automation
• trust in automation foundations
• situational awareness

Information type

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