Touchscreen HMIs are commonly employed as the primary control interface and touch-point of vehicles. However, there has been very little theoretical work to model the demand associated with such devices in the automotive domain. Instead, touchscreen HMIs intended for deployment within vehicles tend to undergo time-consuming and expensive empirical testing and user trials, typically requiring fully-functioning prototypes, test rigs and extensive experimental protocols. While such testing is invaluable and must remain within the normal design/development cycle, there are clear benefits, both fiscal and practical, to the theoretical modelling of human performance. We describe the development of a preliminary model of human performance that makes a priori predictions of the visual demand (total glance time, number of glances and mean glance duration) elicited by in-vehicle touchscreen HMI designs, when used concurrently with driving. The model incorporates information theoretic components based on Hick-Hyman Law decision/search time and Fitts’ Law pointing time, and considers anticipation afforded by structuring and repeated exposure to an interface. Encouraging validation results, obtained by applying the model to a real-world prototype touchscreen HMI, suggest that it may provide an effective design and evaluation tool, capable of making valuable predictions regarding the limits of visual demand/performance associated with in-vehicle HMIs, much earlier in the design cycle than traditional design evaluation techniques. Further validation work is required to explore the behaviour associated with more complex tasks requiring multiple screen interactions, as well as other HMI design elements and interaction techniques. Results are discussed in the context of facilitating the design of in-vehicle touchscreen HMI to minimise visual demand.
Large, D. R., Burnett, G., Crundall, E., van Loon, E., Eren, A. L., & Skrypchuk, L. (2018). Developing predictive equations to model the visual demand of in-vehicle touchscreen HMIs. International Journal of Human-Computer Interaction, 34(1), (1-14). doi:10.1080/10447318.2017.1306940. ISSN 1044-7318