@article{ref1,
title="Assessing drivers' mental model of advanced driver assistance systems using signal detection theory",
journal="Proceedings of the Human Factors and Ergonomic Society annual meeting",
year="2023",
author="Huang, Chunxi and Yan, Song and He, Dengbo",
volume="67",
number="1",
pages="1204-1211",
abstract="Previous studies evaluated drivers' knowledge of advanced driver assistance systems (ADAS) using different kinds of percent-correctness-based mental model scores (MMS), which makes cross-study comparisons difficult. To resolve this issue, our study explored the use of sensitivity (i.e., d-prime (d')) and response bias (i.e., criterion location (c)) in signal detection theory (SDT) as a measure of drivers' ADAS mental models. Based on the data collected from a survey among 287 ADAS users, regression models were fitted, and it was found that d' and c accounted for a large variance when estimating drivers' ADAS mental models as measured by MMSs (adjusted R2 > 0.8). Further, predictors of MMSs were also predictors of d' and c, but d' and c include additional information that was not covered in MMSs. These findings support the usage of d' and c as standard metrics for assessing drivers' ADAS mental models in future research.<p /> <p>Language: en</p>",
language="en",
issn="2169-5067",
doi="10.1177/21695067231193671",
url="http://dx.doi.org/10.1177/21695067231193671"
}