Do driver’s characteristics, system performance, perceived safety, and trust influence how drivers use partial automation? A structural equation modelling analysis

Nordhoff, Sina; Stapel, Jork; He, Xiaolin; Gentner, Alexandre; Happee, Riender · 2023 · Crossref

DOI: 10.3389/fpsyg.2023.1125031

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Summary

This study investigates how driver characteristics, system performance, perceived safety, and trust influence the use of SAE Level 2 partial automation (Adaptive Cruise Control and Lane Keeping Assist). Motivated by the safety risks of overtrust (misuse) and undertrust (disuse), the research aims to clarify the factors driving actual usage behavior among experienced users, addressing a gap in literature that often relies on simulator studies with naïve participants. The researchers conducted an online survey targeting actual users of partially automated vehicles, recruiting via Tesla supercharging stations, online forums, and internal communications at Toyota Motor Europe. After filtering for respondents with access to relevant automation features, 628 valid responses were analyzed. The study employed Structural Equation Modeling (SEM) to test hypotheses derived from technology acceptance models. Variables included socio-demographics, driving experience, personality traits (using a short 10-item Big Five scale), system performance ratings, perceived safety, trust, and secondary task engagement. Results indicated high adoption rates, with 81% of respondents using automation at least weekly. Users rated system detection capabilities (lane markings and lead vehicles) higher than smooth control performance. Disengagements were primarily driven by a lack of trust or because driving was perceived as fun, rather than boredom or sleepiness. The SEM analysis revealed that trust had a significant positive effect on the propensity for secondary task engagement, whereas perceived safety did not significantly influence use. Regarding driver characteristics, age had no significant effect on perceived safety or trust. Neuroticism negatively correlated with both perceived safety and trust, while extraversion showed no impact. The remaining personality traits (openness, conscientiousness, agreeableness) failed to form valid scales in the confirmatory factor analysis and were excluded from the structural model. The findings suggest that trust, rather than perceived safety, is a critical predictor of how drivers engage with partial automation, specifically influencing their willingness to perform secondary tasks. The lack of significant effects from age and most personality traits challenges assumptions that demographic factors strongly dictate automation acceptance. The study highlights the importance of using actual user data over simulator-based studies and calls for future research to reassess short personality scales and further investigate the nuanced relationship between trust, safety perceptions, and automation use.

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Topics

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

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• Empirical Findings: self report data