TY  - JOUR
PY  - 2023//
TI  - Assessing drivers' mental model of advanced driver assistance systems using signal detection theory
JO  - Proceedings of the Human Factors and Ergonomic Society annual meeting
A1  - Huang, Chunxi
A1  - Yan, Song
A1  - He, Dengbo
SP  - 1204
EP  - 1211
VL  - 67
IS  - 1
N2  - 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>
LA  - en
SN  - 2169-5067
UR  - http://dx.doi.org/10.1177/21695067231193671
ID  - ref1
ER  -