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Objective We controlled participants’ glance behavior while using head-down displays (HDDs) and head-up displays (HUDs) to isolate driving behavioral changes due to use of different display types across different driving environments. Background Recently, HUD technology has been incorporated into vehicles, allowing drivers to, in theory, gather display information without moving their eyes away from the road. Previous studies comparing the impact of HUDs with traditional displays on human performance show differences in both drivers’ visual attention and driving performance. Yet no studies have isolated glance from driving behaviors, which limits our ability to understand the cause of these differences and resulting impact on display design. Method We developed a novel method to control visual attention in a driving simulator. Twenty experienced drivers sustained visual attention to in-vehicle HDDs and HUDs while driving in both a simple straight and empty roadway environment and a more realistic driving environment that included traffic and turns. Results In the realistic environment, but not the simpler environment, we found evidence of differing driving behaviors between display conditions, even though participants’ glance behavior was similar. Conclusion Thus, the assumption that visual attention can be evaluated in the same way for different types of vehicle displays may be inaccurate. Differences between driving environments bring the validity of testing HUDs using simplistic driving environments into question. Application As we move toward the integration of HUD user interfaces into vehicles, it is important that we develop new, sensitive assessment methods to ensure HUD interfaces are indeed safe for driving. 

Augmented-Reality (AR) head-up display (HUD) is one of the promising solutions to reduce distraction potential while driving and performing secondary visual tasks; however, we currently don’t know how to effectively evaluate interfaces in this area. In this study, we show that current visual distraction standards for evaluating in-vehicle displays may not be applicable for AR HUDs. We provide evidence that AR HUDs can afford longer glances with no decrement in driving performance. We propose that the selection of measurement methods for driver distraction research should be guided not only by the nature of the task under evaluation but also by the properties of the method itself. 

The use of augmented reality (AR) with drones in infrastructure inspection can increase human capabilities by helping workers access hard-to-reach areas and supplementing their field of view with useful information. Still unknown though is how these aids impact performance when they are imperfect. A total of 28 participants flew as an autonomous drone while completing a target detection task around a simulated bridge. Results indicated significant differences between cued and un-cued trials but not between the four cue types: none, bounding box, corner-bound box, and outline. Differences in trust amongst the four cues indicate that participants may trust some cue styles more than others. 

The use of augmented reality (AR) with semi-autonomous aerial systems in civil infrastructure inspection offers an extension of human capabilities by enhancing their ability to access hard-to-reach areas, decreasing the physical requirements needed to complete the task, and augmenting their visual field of view with useful information. Still unknown though is how helpful AR visual aids may be when they are imperfect and provide the user with erroneous data. A total of 28 participants flew as an autonomous drone around a simulated bridge in a virtual reality environment and participated in a target detection task. In this study, we analyze the effect of AR cue type across discrete levels of target saliency by measuring performance in a signal detection task. Results showed significant differences in false alarm rates in the different target salience conditions but no significant differences across AR cue types (none, bounding box, corner-bound box, and outline) in terms of hits and misses. 

When navigating via car, developing robust mental representations (spatial knowledge) of the environment is crucial in situations where technology fails, or we need to find locations not included in a navigation system’s database. In this work, we present a study that examines how screen-relative and world-relative augmented reality (AR) head-up display interfaces affect drivers’ glance behavior and spatial knowledge acquisition. Results showed that both AR interfaces have similar impact on the levels of spatial knowledge acquired. However, eye-tracking analyses showed fundamental differences in the way participants visually interacted with different AR interfaces; with conformal-graphics demanding more visual attention from drivers. 

The aim of this work is to examine how augmented reality (AR) head worn displays (HWDs) influence worker task performance in comparison to traditional paper blueprints when assembling three various sized wooden frame walls. In our study, 18 participants assembled three different sized frames using one of the three display conditions (conformal AR interface, tag-along AR interface, and paper blueprints). Results indicate that for large frame assembly, the conformal AR interface reduced assembly errors, yet there were no differences in assembly times between display conditions. Additionally, traditional paper blueprints resulted in significantly faster assembly time for small frame assembly. 

Background: Drivers gather most of the information they need to drive by looking at the world around them and at visual displays within the vehicle. Navigation systems automate the way drivers navigate. In using these systems, drivers offload both tactical (route following) and strategic aspects (route planning) of navigational tasks to the automated SatNav system, freeing up cognitive and attentional resources that can be used in other tasks (Burnett, 2009). Despite the potential benefits and opportunities that navigation systems provide, their use can also be problematic. For example, research suggests that drivers using SatNav do not develop as much environmental spatial knowledge as drivers using paper maps (Waters & Winter, 2011; Parush, Ahuvia, & Erev, 2007). With recent growth and advances of augmented reality (AR) head-up displays (HUDs), there are new opportunities to display navigation information directly within a driver’s forward field of view, allowing them to gather information needed to navigate without looking away from the road. While the technology is promising, the nuances of interface design and its impacts on drivers must be further understood before AR can be widely and safely incorporated into vehicles. Specifically, an impact that warrants investigation is the role of AR HUDS in spatial knowledge acquisition while driving. Acquiring high levels of spatial knowledge is crucial for navigation tasks because individuals who have greater levels of spatial knowledge acquisition are more capable of navigating based on their own internal knowledge (Bolton, Burnett, & Large, 2015). Moreover, the ability to develop an accurate and comprehensive cognitive map acts as a social function in which individuals are able to navigate for others, provide verbal directions and sketch direction maps (Hill, 1987). Given these points, the relationship between spatial knowledge acquisition and novel technologies such as AR HUDs in driving is a relevant topic for investigation. Objectives: This work explored whether providing conformal AR navigational cues improves spatial knowledge acquisition (as compared to traditional HUD visual cues) to assess the plausibility and justification for investment in generating larger FOV AR HUDs with potentially multiple focal planes. Methods: This study employed a 2x2 between-subjects design in which twenty-four participants were counterbalanced by gender. We used a fixed base, medium fidelity driving simulator for where participants drove while navigating with one of two possible HUD interface designs: a world-relative arrow post sign and a screen-relative traditional arrow. During the 10-15 minute drive, participants drove the route and were encouraged to verbally share feedback as they proceeded. After the drive, participants completed a NASA-TLX questionnaire to record their perceived workload. We measured spatial knowledge at two levels: landmark and route knowledge. Landmark knowledge was assessed using an iconic recognition task, while route knowledge was assessed using a scene ordering task. After completion of the study, individuals signed a post-trial consent form and were compensated $10 for their time. Results: NASA-TLX performance subscale ratings revealed that participants felt that they performed better during the world-relative condition but at a higher rate of perceived workload. However, in terms of perceived workload, results suggest there is no significant difference between interface design conditions. Landmark knowledge results suggest that the mean number of remembered scenes among both conditions is statistically similar, indicating participants using both interface designs remembered the same proportion of on-route scenes. Deviance analysis show that only maneuver direction had an influence on landmark knowledge testing performance. Route knowledge results suggest that the proportion of scenes on-route which were correctly sequenced by participants is similar under both conditions. Finally, participants exhibited poorer performance in the route knowledge task as compared to landmark knowledge task (independent of HUD interface design). Conclusions: This study described a driving simulator study which evaluated the head-up provision of two types of AR navigation interface designs. The world-relative condition placed an artificial post sign at the corner of an approaching intersection containing a real landmark. The screen-relative condition displayed turn directions using a screen-fixed traditional arrow located directly ahead of the participant on the right or left side on the HUD. Overall results of this initial study provide evidence that the use of both screen-relative and world-relative AR head-up display interfaces have similar impact on spatial knowledge acquisition and perceived workload while driving. These results contrast a common perspective in the AR community that conformal, world-relative graphics are inherently more effective. This study instead suggests that simple, screen-fixed designs may indeed be effective in certain contexts. 

Full windshield displays (WSDs) have the potential to present imagery across the windshield. Current knowledge on display location has not investigated translucent displays at high eccentricities from the driver's forward view. A simulator study (n=26) was conducted aiming to, (a) investigate the effects of Head-Up Display (HUD) location across the entire windshield on driving performance, and (b) better understand how the visual demand for a complex HUD imagery differs from that for a Head-Down Display (HDD). Lane-keeping was poorer when HUD imagery was furthest from the driver (and for the HDD compared to the HUD). Equally, counts of "unacceptable" driving behaviour were greater for displays furthest from the driver's forward view. Furthermore, drivers preferred HUD imagery that was closer to them. The results indicate that HUD evaluations should account for image location, because of how driver gaze location can impact lateral driving performance.