Delineating Clusters of Learners for Driver Assistance Technologies

Abstract

Driver assistance (DA) technologies pose challenges to the formation and maintenance of drivers’ mental models (i.e., understanding) of their operation. The challenges can be overcome through consumer education, training methods, and interface design strategies, but an understanding of how mental models for DA technologies form and evolve is needed. Therefore, we studied drivers’ experiences under extended real-world driving conditions for the purpose of delineating their mental models of DA technologies. Participants (n = 52) who recently purchased a vehicle with at least two DA technologies were interviewed for approximately six months. Cluster analyses (hierarchical and k-means) of data elements extracted from the interviews (e.g., ratings of mental model complexity, accuracy of technology understanding, trust, perceived usefulness, satisfaction) revealed five different types of learners of DA technologies: expert, skilled, moderate, uninformed, and misinformed. This paper reviews how mental model formation and evolution of DA technologies vary across the five learner types. The results indicate that facilitating efficient and appropriate mental model development could be enhanced by incorporating variations in mental model formation and evolution in future consumer education and design approaches.