@article{ref1,
title="Learning to use automation: behavioral changes in interaction with automated driving systems",
journal="Transportation research part F: traffic psychology and behaviour",
year="2019",
author="Forster, Yannick and Hergeth, Sebastian and Naujoks, Frederik and Beggiato, Matthias and Krems, Josef F. and Keinath, Andreas",
volume="62",
number="",
pages="599-614",
abstract="To evaluate human-machine interfaces for automated driving systems, a robust methodology is indispensable. The present driving simulator study investigated the effect of practice on behavioral measures (i.e., experimenter rating, reaction times, error rate) and the development of the preference-performance relationship for automated driving human-machine interfaces. In a within-subject design, N = 55 participants completed several transitions between manual, Level 2 and Level 3 automated driving. Behavioral measures followed the power law of practice with exception of transitions to manual and error rates for Level 3 automation. After the first block of interactions, preference no longer predicted performance. The preference-performance relationship remained stable after the second block of interactions, which is mainly due to a stabilization in behavioral parameters. To get a deeper insight into the evaluation of human-machine interfaces for automated driving, the results suggest the application of multi-method approaches. Furthermore, we found evidence for the influence of initial interactions for self-reported usability.<p /> <p>Language: en</p>",
language="en",
issn="1369-8478",
doi="10.1016/j.trf.2019.02.013",
url="http://dx.doi.org/10.1016/j.trf.2019.02.013"
}