Evaluating distraction of in-vehicle information systems while driving by predicting total eyes-off-road times with...

Increasingly complex in-vehicle information systems (IVIS) have become available in the automotive vehicle interior. To ensure usability and safety of use while driving, the distraction potential of system-associated tasks is most often analyzed during the development process, either by employing empirical or analytical methods, with both families of methods offering certain advantages and disadvantages. The present paper introduces a method that combines the predictive precision of empirical methods with the economic advantages of analytical methods. Keystroke level modeling (KLM) was extended to a task-dependent modeling procedure for total eyes-off-road times (TEORT) resulting from system use while driving and demonstrated by conducting two subsequent simulator studies. The first study involved the operation of an IVIS by N = 18 participants. The results suggest a good model fit (R(2)Adj. = 0.67) for predicting the TEORT, relying on regressors from KLM and participant age. Using the parameter estimates from study 1, the predictive validity of the model was successfully tested during a second study with N = 14 participants using a version of the IVIS prototype with a revised design and task structure (rPred.-Obs. = 0.58). Possible applications and shortcomings of the approach are discussed.