Subtype Divergences of Trust in Autonomous Vehicles: Towards Optimisation of Driver–Vehicle Trust Management

Abstract

Trust determines public acceptance and uptake of autonomous vehicles (AV). Against popular assumption, trustin-automation is not a unitary construct, but comprises trust subtypes that have different behavioural properties and implications. Understanding trust subtypes-specifically competence-based trust (CT) and integrity-based trust (IT)-is crucial for improving public communication about AVs, analysing trustdependent driver behaviours and designing trust-recovering interfaces. However, these issues have been overlooked in most past research. As a pioneering step, the goal of this research was to analyse how experience with AV failures affect CT and IT. After experience with AV driving errors, both trust subtypes were reduced, with CT showing greater reduction. Structural equation modelling revealed CT to be the primary contributor to acceptance for driving automation, with stronger subsequent impact on preference for fully autonomous (SAE L5) than on semi-autonomous driving (SAE L3). These findings inform that trust-repairing interface should target CT after driving errors, especially for higher automation levels where humans are further removed from the loop. Future directions concerning CT-IT interactions, and the impact of AV anthropomorphic design and connnected vehicle cyber-security on IT are discussed.