Augmented Reality (AR) is defined as “a real-time direct or indirect view of a physical real-world environment that has been enhanced/augmented by adding virtual computer-generated information to it” with the aim to enhance the user’s perception of his environment (Carmigniani & Furht, 2011, p. 3). In the driving context, ARDs project information through the windshield and merge it with the driver’s perspective of the environment, creating the visual impression that the virtual information is part of the environment (Poitschke et al., 2008). This kind of display enables a variety of applications to aid drivers in the execution of the driving task. Previously evaluated ARD-functions include navigation (Kim & Dey, 2009; Medenica, Kun, Paek, & Palinko, 2011; Poitschke et al., 2008), braking assistance (Tönnis, Lange, & Klinker, 2007), hazard warning (Rusch et al., 2013; Schall et al., 2013), or collision warning (Charissis, Papanastasiou, Mackenzie, & Arafat, 2011).
2.3.3.1 Support Potential for Older Drivers
ARDs have a strong potential to support older drivers by compensating for several of the presented age-related functional declines (see Chapter 2.2.1), especially regarding information processing speed, attention capacities, and visual abilities (Färber, 2000; Rusch et al., 2013; Schall et al., 2013). Presenting significant traffic information by superimposing the driver’s field of view instead of providing an extra display could contribute not only to reduce the amount of glances away from the road ahead, as it is already achieved by conventional HUDs, but also to facilitate switching attention between driving environment and displayed information. Additionally, this display structure allows for a very intuitive information design and therefore places comparatively less cognitive demands on drivers (e.g. by minimising working memory demands) (Kim & Dey, 2009). Especially the reduction of cognitive demands is strongly recommended for an age-appropriate design of IVIS (Jahn & Krems, 2013). Since ARDs represent a technological approach to display information during driving, their benefit is directly related to the content presented through this approach. Therefore, an age-specific ARD should be oriented towards the needs and limitation of its target group in terms of the presented information as well. One potential function of an age-specific ARD considered beneficial for older drivers is the early presentation of information about approaching intersections before arrival (prior information) (Davidse, 2006; Davidse et al., 2009; Küting & Krüger, 2002; Rompe, 2012). Corresponding to their tendency to a serial information processing and responding, this would allow them to serially process some of the relevant intersection information before entering this complex environment. This should improve their visual processing of intersections (e.g. reduction of missed traffic signs or road users) by reducing the amount of new information needed while driving through the intersection itself and therefore diminishing the situation’s complexity. The benefit of this approach has already been demonstrated in other contexts, such as the facilitation of identifying critical information in complex auditory environments through auditory prior information (Getzmann, Lewald, & Falkenstein, 2014). However, researches evaluating the effects of ADAS and IVIS have raised the issue that “no technical system will be 100% failure-free” (Mahr & Müller, 2011, p. 120) and therefore perfectly reliable (Naujoks, Kiesel, & Neukum, 2016), motivating studies on the immediate and lasting behavioural effects of occasional system failures. In the case of the IVIS evaluated in this study, the most apparent system failure would be the presentation of inaccurate information about approaching intersections. On an immediate level, this failure could increase intersection complexity due to the mismatch between ARD-information and environment and therefore complicate drivers’ visual processing of intersections. This effect might be more pronounced for older drivers given their functional declines (Falkenstein & Poschadel, 2008), which could cause a greater distraction by inaccurate information
presented via an ARD. In previous studies on in-vehicle warning systems, occasionally false information lastingly reduced drivers’ compliance and therefore system effectiveness (Cotté, Meyer, & Coughlin, 2001; Naujoks et al., 2016). Accordingly, occasional inaccurate information might also lastingly reduce the benefits of the ARD evaluated in this study.
2.3.3.2 Speed and Accuracy of Perceiving Traffic Situations
Prior information about complex traffic situations presented via an ARD are expected to enhance drivers’ ability of perceiving these traffic situations based on a more effective attention allocation. The ability to perceive traffic situations accurately and quickly enough is considered a necessary precondition for a safe driving performance (Crundall & Underwood, 2011). Particularly relevant for driving is visual processing speed, “the amount of time needed to make a correct judgement about a visual stimulus” (Owsley, 2013, p. 52). Limitations in this field lead to difficulties detecting traffic information and responding appropriately under the time pressure applied by the dynamic of road traffic (Eby & Molnar, 2012; Owsley, 2013). Based on the established conclusion that accidents occur because drivers “fail to look at the right thing in the right time” (Lee, 2008, p. 525), an ARD supporting drivers’ visual processing of traffic situations is expected to improve driving performance as well. The connection between speed and accuracy of perceiving traffic situations and driving performance can also be explained within the framework of the ECOM (see Chapter 2.1.1). In terms of this model, the presented ARD-concept supports the execution of the driving task on the monitoring level by providing information about approaching driving situations and traffic regulations earlier and guiding the driver’s attention to relevant parts of the driving environment. Since this generates more time available for the control processes on this level, it should result in a more appropriate output in terms of the goals selected for the next lower regulation level. Based on the interactive nature of the ECOM, supporting activities on the monitoring level will positively affect the performance on the regulation level of the driving task in terms of selecting safer driving manoeuvres (e.g. braking instead of crossing the intersection based on a reduction of missed road users at the intersection). However, these theoretical considerations still have to be verified empirically.