Citation

Woide M, Stiegemeier D, Pfattheicher S, Baumann M. Transp. Res. F Traffic Psychol. Behav. 2021; 83: 424-439.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.trf.2021.11.003

PMID

unavailable

Abstract

The development of partially and highly autonomous vehicles that take over parts of the driving task will result in changes in e.g. the responsibilities, interface and system design, task allocation and communication between driver and automated vehicle. To support this change and the increasing space of cooperative possibilities between driver and vehicle, generally accepted design principles and preconditions for successful driver-vehicle cooperation (Directability, Mutual Predictability, Joint Goals and Mutual Task-Dependency) were defined. However, research lacks validated scales measuring the proposed basic principles of driver-vehicle cooperation. Furthermore, a theory is missing that links those basic principles with a theory that enables an understanding of the influence of drivers' perception of the autonomous vehicle and context on driver-vehicle cooperation. Therefore, this work links the basic theoretical principles of driver-vehicle cooperation with the social psychological Theory of Interdependence and their dimensions (Conflict, Power, Mutual Dependence, Information Certainty, Future Interdependence). Filling the gap of missing validated scales for the principles of driver-vehicle cooperation, this study provides the development and validation of the Human-Machine-Interaction-Interdependence (HMII) questionnaire. In two studies, the new HMII questionnaire to measure drivers' perception in driver-vehicle cooperation was developed. In the first study (n = 94), items for the perception of the situation were transferred from the original Theory of Interdependence to the driver-vehicle cooperation. A 7-dimension model was identified via Explorative Factor Analysis. In the second study (n = 314), the model and items were validated via confirmatory factor analysis. A seven-factor model (Power, Conflict, Mutual Dependence Information Certainty: System to Human, Information Certainty (two levels) Future Interdependence (two levels) with 33 items showed a good fit to the data, chi2 = 841, p < .001, adjusted chi2 = 1.77, SRMR = 0.071. In sum, this questionnaire can help designers evaluate the impact of their system designs on driver perceptions of the driver-vehicle cooperation.

Language: en

Keywords

Autonomous vehicle; Driver-vehicle cooperation; Human-machine interaction; Interdependence theory; Questionnaire