Exploring Factors Related to Drivers’ Mental Model of and Trust in Advanced Driver Assistance Systems Using an ABN-Based Mixed Approach

Drivers’ appropriate mental models of and trust in advanced driver assistance systems (ADAS) are essential to driving safety in vehicles with ADAS. Although several previous studies evaluated drivers’ ADAS mental models of and trust in adaptive cruise control and lane-keeping assist systems, research gaps still exist. Specifically, recent developments in ADAS have made more advanced functions available but they have been under-investigated. Furthermore, the widely adopted proportional correctness-based scores may not differentiate drivers’ objective ADAS mental model and subjective bias toward the ADAS. Finally, most previous studies adopted only regression models to explore the influential factors and thus may have ignored the underlying association among the factors. Therefore, our study aimed to explore drivers’ mental models of and trust in emerging ADAS by using the sensitivity (i.e., d’ ) and response bias (i.e., c ) measures from the signal detection theory. We modeled the data from 287 drivers using additive Bayesian network (ABN) and further interpreted the graph model using regression analysis. We found that different factors might be associated with drivers’ objective knowledge of ADAS and subjective bias toward the existence of functions/limitations. Furthermore, drivers’ subjective bias was more associated with their trust in ADAS compared to objective knowledge. The findings from our study provide new insights into the influential factors on drivers’ mental models of ADAS and better reveal how mental models can affect trust in ADAS. It also provides a case study on how the mixed approach with ABN and regression analysis can model observational data.