An extended keystroke level model (KLM) for predicting the visual demand of in-vehicle information systems

Abstract

To assess the potential distraction of In-Vehicle Information Systems (IVIS), simple, low cost evaluation methods are required for use in early design stages. The occlusion technique evaluates IVIS tasks in interrupted vision conditions, aiming to predict likely visual demand. However, the technique necessitates performance-focused user trials utilising robust prototypes, and consequently has limitations as an economic evaluation method. HCI practitioners view the Keystroke Level Model (KLM) as a reliable and valid means of modelling human performance, not requiring empirical trials or working prototypes. This paper proposes an extended KLM, which aims to predict measures based on the occlusion protocol. To validate the new method, we compared results of an occlusion study with predictions based on the assumptions of the extended KLM. Analysis revealed significant correlations between observed and predicted results (R=0.93-0.98) and low error rates (7-13%). In conclusion, the extended KLM shows considerable merit as a first-pass design tool.