The findings can be utilized in context-aware distraction mitigation systems, human-automated vehicle interaction, road speed prediction and design, as well as in the testing of visual in-vehicle tasks for inappropriate in-vehicle glancing behaviors in any dynamic traffic scenario for which appropriate individual occlusion distances can be defined.

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.

Dynamic representation of functional brain networks involved in the sequence analysis of functional connectivity graphs of the brain (FCGB) gains advances in uncovering evolved interaction mechanisms. However, most of the networks, even the event-related ones, are highly heterogeneous due to spurious interactions, which bring challenges to revealing the change patterns of interactive information in the complex dynamic process. In this paper, we propose a network entropy (NE) method to measure connectivity uncertainty of FCGB sequences to alleviate the spurious interaction problem in dynamic network analysis to realize associations with different events during a complex cognitive task. The proposed dynamic analysis approach calculated the adjacency matrices from ongoing electroencephalpgram (EEG) in a sliding time-window to form the FCGB sequences. The probability distribution of Shannon entropy was replaced by the connection sequence distribution to measure the uncertainty of FCGB constituting NE. Without averaging, we used time frequency transform of the NE of FCGB sequences to analyze the event-related changes in oscillatory activity in the single-trial traces during the complex cognitive process of driving. Finally, the results of a verification experiment showed that the NE of the FCGB sequences has a certain time-locked performance for different events related to driver fatigue in a prolonged driving task. The time errors between the extracted time of high-power NE and the recorded time of event occurrence were distributed within the range [−30 s, 30 s] and 90.1% of the time errors were distributed within the range [−10 s, 10 s]. The high correlation (r = 0.99997, p < 0.001) between the timing characteristics of the two types of signals indicates that the NE can reflect the actual dynamic interaction states of brain. Thus, the method may have potential implications for cognitive studies and for the detection of physiological states.

In-car infotainment systems require icons that enable fluent cognitive information processing and safe interaction while driving. An important issue is how to find an optimised set of icons for different functions in terms of semantic distance. In an optimised icon set, every icon needs to be semantically as close as possible to the function it visually represents and semantically as far as possible from the other functions represented concurrently. In three experiments (N = 21 each), semantic distances of 19 icons to four menu functions were studied with preference rankings, verbal protocols, and the primed product comparisons method. The results show that the primed product comparisons method can be efficiently utilised for finding an optimised set of icons for time-critical applications out of a larger set of icons. The findings indicate the benefits of the novel methodological perspective into the icon design for safety-critical contexts in general.

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