Personality Openness Predicts Driver Trust in Automated Driving

Li, Wenmin; Yao, Nailang; Shi, Yanwei; Nie, Weiran; Zhang, Yuhai; Li, Xiangrong; Liang, Jiawen; Chen, Fang; Gao, Zaifeng · 2020 · Crossref

DOI: 10.1007/s42154-019-00086-w

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Summary

This study investigates the relationship between driver personality traits and trust in Level 2 automated driving (AD) systems, addressing a gap in literature regarding individual differences in automation trust. While prior research has examined system transparency and social cues, few studies have explored how stable personality characteristics influence trust in AD. The authors hypothesized that specific Big Five personality traits would correlate with trust levels, particularly predicting a negative correlation for Openness, as AD removes the intellectual engagement often preferred by individuals high in this trait. The experiment involved 28 university students with driver’s licenses but no prior AD experience. Participants completed the Chinese Big Five Personality Inventory Brief and then engaged in a 50 km simulated city drive using a 6-degree-of-freedom driving simulator. During the automated driving phase, participants performed a non-driving-related mathematical addition task on a tablet. The simulation included 24 emergent events (e.g., sudden braking, pedestrian crossings) where the AD system might fail, requiring drivers to take over control. Data collected included subjective trust scores via questionnaire, gaze behavior (monitoring frequency and ratio toward driving-related areas), and driving behavior (take-over rate and distance). Results revealed a significant negative correlation between the Openness personality trait and driver trust in the AD system across all three measures. Participants with higher Openness scores reported lower subjective trust, exhibited higher monitoring frequencies and ratios, and demonstrated higher take-over rates and earlier take-over distances. No significant correlations were found between trust and the other four personality traits (Neuroticism, Conscientiousness, Agreeableness, and Extraversion) in the primary analyses, although Extraversion showed a marginal negative correlation with subjective trust that did not persist in partial correlations controlling for driving experience. The findings indicate that driver personality, specifically Openness, significantly predicts trust in automated driving systems. Individuals high in Openness tend to distrust AD systems more, likely because the automation reduces the cognitive engagement and novelty they prefer. This suggests that personality is a critical factor in human-machine interaction design for AD. The study implies that personalized HMI strategies may be necessary to calibrate trust levels for different personality types, ensuring safety by preventing under-trust in drivers who are naturally skeptical due to their personality profiles.

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Topics

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• trust calibration
• trust in automation foundations
• automation
• acceptance adoption
• automation surprise
• personality driving

Information type

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• Empirical Findings: self report data
• Methodological Resource: tool software, validation psychometrics