Modeling visual sampling on in-car displays: The challenge of predicting safety-critical lapses of control

Abstract

a b s t r a c tIn this article, we study how drivers interact with in-car interfaces, particularly by focusing on understanding driver in-car glance behavior when multitasking while driving. The work focuses on using an incar touch screen to find a target item from a large number of unordered visual items spread across multiple screens. We first describe a cognitive model that aims to represent a driver's visual sampling strategy when interacting with an in-car display. The proposed strategy assumes that drivers are aware of the passage of time during the search task; they try to adjust their glances at the display to a time limit, after which they switch back to the driving task; and they adjust their time limits based on their performance in the current driving environment. For visual search, the model assumes a random starting point, inhibition of return, and a search strategy that always seeks the nearest uninspected item. We validate the model's predictions with empirical data collected in two driving simulator studies with eye tracking. The results of the empirical study suggest that the visual design of in-car displays can have a significant impact on the probability of distraction. In particular, the results suggest that designers should try to minimize total task durations and the durations of all visual encoding steps required for an in-car task, as well as minimize the distance between visual display elements that are encoded one after the other. The cognitive model helps to explain gaze allocation strategies for performing in-car tasks while driving, and thus helps to quantify the effects of task duration and visual item spacing on safety-critical in-car glance durations.

Context-sensitive distraction warnings – Effects on drivers׳ visual behavior and acceptance

“…tools (Kujala and Salvucci, 2015), driver education , reactive in-car driver assistance (e.g., lane-keeping assistants) and feedback systems (Donmez et al, 2007), as well as legislative and governmental regulations (NHTSA, 2013b) may help in reducing the negative effects of driver distraction by in-car activities, there is additional demand for fast and cost-effective counter-measures that can be easily deployed by a driver. According to our study, mobile applications aimed to supervise the use of the smart phone while driving and aiding the driver to place more attention on road seems to be a one viable and acceptable option.…”

Modelling Learning of New Keyboard Layouts

“…The EMMA model has been successfully used for simulating gaze patterns in various tasks, such as reading [55], menu search [55], and driving [56,37]. Its main benefit is that it can encode targets without necessarily having to make a saccade.…”

Touchscreen interfaces in context: A systematic review of research into touchscreens across settings, populations, and implementations

“…The five motor vehicle studies (Irune & Burnett, 2007;Kim et al, 2014;Kujala & Salvucci, 2015) evaluated target size, target spacing, menu size, and menu arrangement in touchscreen IVIS interfaces on driving performance and secondary task performance; these studies are evaluated in full under RQ2.…”

“…(2013) compared capacitive and resistive IVIS touch displays, as discussed above under RQ1. Five studies (Irune & Burnett, 2007;Kim et al, 2014;Kujala & Salvucci, 2015) evaluated touch interfaces in IVIS design, including target size, target spacing, menu size, and menu arrangement. Irune and Burnett (2007) reported two studies that evaluated control cluster size and arrangement, control size, and control spacing; they found that higher total control count increased task time, visual demand, and perceived difficulty, identifying clusters of 16 controls or more as resulting in total glance times above 4 seconds; tests of cluster arrangements indicated that increased vertical cluster size in particular yields longer task time and longer total glance time, while increased horizontal cluster size has only a lesser effect.…”