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

• Published in: Transportation research. Part F, Traffic psychology and behaviour Vol. 62; pp. 599 - 614
• Main Authors: Forster, Yannick, Hergeth, Sebastian, Naujoks, Frederik, Beggiato, Matthias, Krems, Josef F., Keinath, Andreas
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.04.2019; Elsevier Science Ltd
• Subjects: Automated driving; Automation; Behavioral measures; Human behavior; Human-computer interaction; Human-machine interface; Man-machine interfaces; Method development; Power-law of learning; Systems analysis; Transportation; Power-law of learning; Behavioral measures; Automated driving; Method development; Human-machine interface
• ISSN: 1369-8478; 1873-5517
• DOI: 10.1016/j.trf.2019.02.013

•Behavioral interaction with driving automation follows the power law of learning.•The preference-performance relationship changes over time.•Importance of initial interactions for self-reported satisfaction.•Methodological recommendation for behavioral measure application. 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.