Learning to use automation: Behavioral changes in interaction with automated driving systems

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.