Effects of in-car task features on driver’s visual

driving scenario and drivers’ individual glancing behaviors. The distraction potential testing method of Kujala and Mäkelä (2016) may deliver the need.

However, since the method is recent, its reliability and validity should be evaluated. Moreover, this kind of distraction potential testing method together with proper operationalization of driver inattention in relation to a baseline of attentive driving could provide increased comprehension regarding how distracting different in-car task features and interaction methods are for drivers.

For the reasons above, the new testing method of Kujala and Mäkelä (2016) is used in the studies included in this dissertation.

2.5 Effects of in-car task features on driver’s visual distraction potential

The ample number of technologies used while driving have evoked a number of studies examining the effects of in-car task features on a driver’s visual inattention and distraction. Nevertheless, it is not clear which exact user interface design factors have an effect on a driver’s visual inattention and how substantial these effects are. However, some general features have been studied which give indications of how distracting they are for drivers. Still, more specific knowledge is needed to understand how these design features affect visual distraction. It should be noted that this is not an extensive review; the studies are selected here for their relevance to this dissertation.

Text entry methods and their effects have been studied earlier. According to the studies (e.g., Crandall & Chaparro, 2012; McKeever et al., 2013; Perlman et al., 2019; Reimer et al., 2014; Tippey et al., 2017; Tsimhoni et al., 2004), text entry with a touch screen keyboard is among the most visually distracting in-car tasks

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for drivers. Several studies have also indicated that a voice recognition-based text entry (or speech-to-text function) is significantly less distracting than a keyboard text entry (e.g., Beckers et al., 2017; He et al., 2014; He et al., 2015; Tippey et al., 2017; Tsimhoni et al., 2004). However, as Reimer and Mehler (2013) pointed out, it is reasonable to take into account that against common belief, the voice-guided systems usually also include some visual-manual interactions, which may be distractive for drivers.

Another design factor that may diminish a driver’s visual distraction is utilizing a read-aloud function as an interaction method. Read-aloud functions read selected text aloud. According to the study of Owens et al. (2011), a read-aloud function is not causing longer off-road glances compared to baseline driving. However, there is not much published research that examines how distracting the read-aloud function (measured with glance duration data) is in the driving context. Conversely to Owens et al. (2011), other studies that are not based on glance durations have concluded that the read-aloud function may not be distraction-free either (Jamson et al., 2004; Lee et al., 2001).

Handwriting is one method to conduct text entries as well. However, there is no extensive literature concerning handwritten text inputs in the automotive context. For example, Burnett et al. (2005) found out that handwriting was a faster text input method than a keyboard (n.b., when writing with a non-preferred left hand). In addition, Kern et al. (2009) studied to where the handwriting surface should be located in the car cockpit. Hence, broader distraction testing of the handwriting method, incorporating in-car glance measurements, seems to be lacking.

In order to reduce drivers’ visual distraction, head-up display (HUD) technologies have been in the research focus, too. Head-up displays may have notable potential to reduce visual distraction compared to head-down displays (HDD). For example, Weinberg et al. (2011) noticed that, when using HDD, the number of in-vehicle glances doubled compared to when using HUD. Lagoo et al. (2019) found out that using HUD compared to HDD indicated a 45%

improvement in collision avoidance. Topliss et al. (2020) observed that, compared to HUD, HDDs led to a higher percentage of unsafe driving performance. In addition, Villalobos-zúñiga et al. (2016) demonstrated that a combination of a physical keypad and HUD enabled drivers to maintain visual attention on the road up to 64% more compared to a touch screen keyboard.

However, HUD may cause negative gaze concentration effects compared to baseline driving without any secondary tasks (Victor et al., 2005).

Intuitively thinking, a bigger screen size enables more efficient task performance and, in general, that has been ensured with studies by Hancock et al. (2015) and Raptis et al. (2013). In the automotive context, Gaffar and Kouchak (2017) studied drivers’ reaction times while selecting the target icon on either 7”

or 10” screen. They did not find differences in reaction times between those two relatively large screens. Unfortunately, they did not measure glance durations.

Similarly, previous studies have not extensively dealt with the effects of screen orientation (landscape versus portrait) on visual distraction in detail either,

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which could be one factor affecting visual distraction. According to one study (Lasch & Kujala, 2012), the screen’s orientation had no effect on in-car glance durations.

Another intuitive thought is that bigger button sizes reduce visual distraction. Nevertheless, Feng et al. (2018) concluded that there is no significant difference on driver’s visual distraction between medium and large button sizes.

A significantly longer total eyes off-road time, however, was found between small button sizes compared to medium or large buttons. Though, it should be noted that these small buttons were smaller (side length of 14 mm) than any recommendation (e.g., Monterey Technologies Inc, 1996) suggest.

According to previous literature, when conducting in-car tasks, page-by-page scrolling with a simple swiping gesture is visually least demanding for drivers compared to button presses or kinetic scrolling (Kujala, 2013; Lasch &

Kujala, 2012). In contrary, in Large et al.'s (2013) study where participants were asked to find specific words from a list presented in touch screen, page-by-page scrolling was visually the most distracting, whereas kinetic scrolling was less distracting. However, in Large et al.’s (2013) study, the lists were organized alphabetically which may explain the opposite result as in the previously mentioned studies. When words are in alphabetical order, finding the right word can perhaps be done faster with kinetic scrolling than scrolling page-by-page. If the searched word starts with, for instance, letter T, it is quicker to get there with kinetic scrolling than with page-by-page scrolling. Lasch and Kujala (2012) also concluded that seven items per screen (at least on 4” screen) is too many items for in-car use. In addition, Kujala and Saariluoma (2011) found out that in-car devices’ list-style menu structure led to a smaller variance in in-car glance durations than grid-style menu structure. These findings concerning scrolling methods, number of items, and menu structures could be design factors to diminish drivers’ visual distraction.

Well-designed task structures are yet another design factor that could decrease drivers’ visual distraction while conducting secondary in-car tasks.

Task structure means "how a task breaks down into smaller subtasks" (Salvucci

& Kujala, 2016). According to previous observations, people tend to switch tasks at subtask boundaries (e.g., Janssen et al., 2012; Lee et al., 2015; Lee & Lee, 2019;

Salvucci & Kujala, 2016), for example typing one word or dialing a phone number in chunks at a time (Janssen et al., 2012) instead of one letter or one number. This attention switching at subtask boundaries reduces cognitive load (e.g., Bailey &

Iqbal, 2008; Janssen et al., 2012). If the task structures are designed in a way that enables the use of subtask boundaries, this could be beneficial for the drivers to diminish visual distraction.

The literature on in-car tasks’ interaction methods has certainly provided several suggestions on how these general features affect drivers’ visual inattention. Typically, these kinds of studies compare the distraction potential of the tested tasks’, which provides information on how distractive those tasks might be in relation to each other tested task. Hence, this does not necessarily provide information on the tested task’s distraction potential per se, compared

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against a baseline of attentive driving. Further, if the measures used are unreliable, even these relational differences in the task’s distraction potential can be false. However, in order to design visually low demanding applications for drivers, more scientific knowledge is needed on how applications’ different features and interaction methods affect drivers’ visual inattention.

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In order to answer the posited research questions, we conducted experiments, scrutinized their results and reported the findings in six articles. This chapter presents those six articles as they form the empirical part of this doctoral dissertation. The original articles are included at the end of this dissertation and therefore only the overviews of the included articles are presented here. The overviews include the aim and the contributions of the articles from the perspective of this dissertation.

3.1 Article I

Grahn, H., Kujala, T., Silvennoinen, J., Leppänen, A., & Saariluoma, P. (2020).

Expert drivers’ prospective thinking-aloud to enhance automated driving technologies – Investigating uncertainty and anticipation in traffic. Accident Analysis & Prevention, 146, 105717.

As said previously, to understand inattention, we should first understand what attention. Therefore, we set out to more deeply investigate targets of attention in the driving context. To do that, we selected experts in the driving domain – that is, driving instructors – and with the prospective thinking-aloud method examined their anticipations, to what and where these experts attend, and how they act upon in real traffic. The prospective thinking-aloud method was developed precisely for this study.

First, we validated the expertise of the driving instructors with a hazard prediction test (N = 36) in our laboratory to substantiate that they are able to anticipate and predict unfolding hazardous events at a better rate than inexperienced or ordinary (mixed group in the article) drivers. After the validation, the subsample of experts (N = 6) drove on public roads while prospectively thinking aloud their anticipations of unfolding traffic events. These anticipations