Isolating the Effect of Off-Road Glance Duration on Driving Performance: An Exemplar Study Comparing HDD and HUD in Different Driving Scenarios.

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.

The Effect of Context Switching, Focal Switching Distance, Binocular and Monocular Viewing, and Transient Focal Blur on Human Performance in Optical See-Through Augmented Reality.

In optical see-through augmented reality (AR), information is often distributed between real and virtual contexts, and often appears at different distances from the user. To integrate information, users must repeatedly switch context and change focal distance. If the user's task is conducted under time pressure, they may attempt to integrate information while their eye is still changing focal distance, a phenomenon we term transient focal blur. Previously, Gabbard, Mehra, and Swan (2018) examined these issues, using a text-based visual search task on a one-eye optical see-through AR display. This paper reports an experiment that partially replicates and extends this task on a custom-built AR Haploscope. The experiment examined the effects of context switching, focal switching distance, binocular and monocular viewing, and transient focal blur on task performance and eye fatigue. Context switching increased eye fatigue but did not decrease performance. Increasing focal switching distance increased eye fatigue and decreased performance. Monocular viewing also increased eye fatigue and decreased performance. The transient focal blur effect resulted in additional performance decrements, and is an addition to knowledge about AR user interface design issues.

Assessing Distraction Potential of Augmented Reality Head-Up Displays for Vehicle Drivers.

OBJECTIVE: To develop a framework for quantifying the visual and cognitive distraction potential of augmented reality (AR) head-up displays (HUDs). BACKGROUND: AR HUDs promise to be less distractive than traditional in-vehicle displays because they project information onto the driver's forward-looking view of the road. However, AR graphics may direct the driver's attention away from critical road elements. Moreover, current in-vehicle device assessment methods, which are based on eyes-off-road time measures, cannot capture this unique challenge. METHOD: This article proposes a new method for the assessment of AR HUDs by measuring driver gaze behavior, situation awareness, confidence, and workload. An experimental user study (n = 24) was conducted in a driving simulator to apply the proposed method for the assessment of two AR pedestrian collision warning (PCW) design alternatives. RESULTS: Only one of the two tested AR interfaces improved driver awareness of pedestrians without visually and cognitively distracting drivers from other road elements that were not augmented by the display but still critical for safe driving. CONCLUSION: Our initial human-subject experiment demonstrated the potential of the proposed method in quantifying both positive and negative consequences of AR HUDs on driver cognitive processes. More importantly, the study suggests that AR interfaces can be informative or distractive depending on the perceptual forms of graphical elements presented on the displays. APPLICATION: The proposed methods can be applied by designers of in-vehicle AR HUD interfaces and be leveraged by designers of AR user interfaces in general.

What, How, and Why are Visual Assets Used in Industrial Augmented Reality? A Systematic Review and Classification in Maintenance, Assembly, and Training (From 1997 to 2019).

Industrial Augmented Reality (iAR) has demonstrated its advantages to communicate technical information in the fields of maintenance, assembly, and training. However, literature is scattered among different visual assets (i.e., AR visual user interface elements associated with a real scene). In this work, we present a systematic literature review of visual assets used in these industrial fields. We searched five databases, initially finding 1757 papers. Then, we selected 122 iAR papers from 1997 to 2019 and extracted 348 visual assets. We propose a classification for visual assets according to (i) what is displayed, (ii) how it conveys information (frame of reference, color coding, animation), and, (iii) why it is used. Our review shows that product models, text and auxiliary models are, in order, the most common, with each most often used to support operating, checking and locating tasks respectively. Other visual assets are scarcely used. Product and auxiliary models are commonly rendered world-fixed, color coding is not used as often as expected, while animations are limited to product and auxiliary model. This survey provides a snapshot of over 20 years of literature in iAR, useful to understand established practices to orientate in iAR interface design and to present future research directions.

A Perceptual Color-Matching Method for Examining Color Blending in Augmented Reality Head-Up Display Graphics.

Augmented reality (AR) offers new ways to visualize information on-the-go. As noted in related work, AR graphics presented via optical see-through AR displays are particularly prone to color blending, whereby intended graphic colors may be perceptually altered by real-world backgrounds, ultimately degrading usability. This work adds to this body of knowledge by presenting a methodology for assessing AR interface color robustness, as quantitatively measured via shifts in the CIE color space, and qualitatively assessed in terms of users' perceived color name. We conducted a human factors study where twelve participants examined eight AR colors atop three real-world backgrounds as viewed through an in-vehicle AR head-up display (HUD); a type of optical see-through display used to project driving-related information atop the forward-looking road scene. Participants completed visual search tasks, matched the perceived AR HUD color against the WCS color palette, and verbally named the perceived color. We present analysis that suggests blue, green, and yellow AR colors are relatively robust, while red and brown are not, and discuss the impact of chromaticity shift and dispersion on outdoor AR interface design. While this work presents a case study in transportation, the methodology is applicable to a wide range of AR displays in many application domains and settings.

Virtual Replicas of Real Places: Experimental Investigations.

As virtual reality (VR) technology becomes cheaper, higher-quality, and more widely available, it is seeing increasing use in a variety of applications including cultural heritage, real estate, and architecture. A common goal for all these applications is a compelling virtual recreation of a real place. Despite this, there has been very little research into how users perceive and experience such replicated spaces. This article reports the results from a series of three user studies investigating this topic. Results include that the scale of the room and large objects in it are most important for users to perceive the room as real and that non-physical behaviors such as objects floating in air are readily noticeable and have a negative effect even when the errors are small in scale.

Determining the impact of augmented reality graphic spatial location and motion on driver behaviors.

While researchers have explored benefits of adding augmented reality graphics to vehicle displays, the impact of graphic characteristics have not been well researched. In this paper, we consider the impact of augmented reality graphic spatial location and motion, as well as turn direction, traffic presence, and gender, on participant driving and glance behavior and preferences. Twenty-two participants navigated through a simulated environment while using four different graphics. We employed a novel glance allocation analysis to differentiate information likely gathered with each glace with more granularity. Fixed graphics generally resulted in less visual attention and more time scanning for hazards than animated graphics. Finally, the screen-fixed graphic was preferred by participants over all world-relative graphics, suggesting that graphic spatially integration into the world may not always be necessary in visually complex urban environments like those considered in this study.

Identification of Design Criteria to Improve Patient Care in Electronic Health Record Downtime.

OBJECTIVE: Design criteria specifications (needs, obstacles, and context-of-use considerations) for continuing safe and efficient patient care activities during downtime were identified by using phenomenological analysis. METHODS: Interview transcripts from medical personnel who had experience with downtime incidents were examined using a phenomenological approach. This process allowed for the identification of design criteria for performing downtime patient care activities. RESULTS: A substantial variation in criteria was found from participants in different roles. The differences suggest opportunities to address downtime that may require attention to individual roles. CONCLUSIONS: Workload distribution and communication are significant issues in patient care during downtime. There may not be an equal work distribution, leading to an increased workload for some personnel during downtime. Phenomenological analysis was completed after participants were interviewed, indicating it is a viable post hoc approach. Some downtime criteria were identified as potential guidelines for the development of better downtime contingency plans.

The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities.

The PathoSystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center funded by the National Institute of Allergy and Infectious Diseases (https://www.patricbrc.org). PATRIC supports bioinformatic analyses of all bacteria with a special emphasis on pathogens, offering a rich comparative analysis environment that provides users with access to over 250 000 uniformly annotated and publicly available genomes with curated metadata. PATRIC offers web-based visualization and comparative analysis tools, a private workspace in which users can analyze their own data in the context of the public collections, services that streamline complex bioinformatic workflows and command-line tools for bulk data analysis. Over the past several years, as genomic and other omics-related experiments have become more cost-effective and widespread, we have observed considerable growth in the usage of and demand for easy-to-use, publicly available bioinformatic tools and services. Here we report the recent updates to the PATRIC resource, including new web-based comparative analysis tools, eight new services and the release of a command-line interface to access, query and analyze data.

Effects of AR Display Context Switching and Focal Distance Switching on Human Performance.

In augmented reality (AR) environments, information is often distributed between real world and virtual contexts, and often appears at different distances from the user. Therefore, to integrate the information, users must repeatedly switch context and refocus the eyes. To focus at different distances, the user's eyes must accommodate, which when done repeatedly can cause eyestrain and degrade task performance. An experiment was conducted that examined switching context and focal distance between a real and an AR environment, using a text-based visual search task and a monocular optical see-through AR display. Both context switching and focal distance switching resulted in significantly reduced performance. In addition, repeatedly performing the task caused visual fatigue to steadily increase. Performance was particularly poor for virtual text presented at optical infinity, and for target letters that participants tried to read before their eyes had completely accommodated to a new focal distance. The results show that context switching and focal distance switching are important AR user interface design issues.

Influences of augmented reality head-worn display type and user interface design on performance and usability in simulated warehouse order picking.

Limited information is available regarding the effective use of workplace head-worn displays (HWD), especially the choices of HWD types and user interface (UI) designs. We explored how different HWD types and UI designs affect perceived workload, usability, visual discomfort, and job performance during a simulated warehouse job involving order picking and part assembly. Sixteen gender-balanced participants completed the simulated job in all combinations of two HWD types (binocular vs. monocular) and four UIs, the latter of which manipulated information mode (text-vs. graphic-based) and information availability (always-on vs. on-demand); a baseline condition was also completed (paper pick list). Job performance, workload, and usability were more affected by UI designs than HWD type. For example, the graphic-based UI reduced job completion time and number of errors by ∼13% and ∼59%, respectively. Participants had no strong preference for either of the HWD types, suggesting that the physical HWD designs tested are suboptimal.

AR DriveSim: An Immersive Driving Simulator for Augmented Reality Head-Up Display Research.

Optical see-through automotive head-up displays (HUDs) are a form of augmented reality (AR) that is quickly gaining penetration into the consumer market. Despite increasing adoption, demand, and competition among manufacturers to deliver higher quality HUDs with increased fields of view, little work has been done to understand how best to design and assess AR HUD user interfaces, and how to quantify their effects on driver behavior, performance, and ultimately safety. This paper reports on a novel, low-cost, immersive driving simulator created using a myriad of custom hardware and software technologies specifically to examine basic and applied research questions related to AR HUDs usage when driving. We describe our experiences developing simulator hardware and software and detail a user study that examines driver performance, visual attention, and preferences using two AR navigation interfaces. Results suggest that conformal AR graphics may not be inherently better than other HUD interfaces. We include lessons learned from our simulator development experiences, results of the user study and conclude with limitations and future work.

Augmented Reality Interface Design Approaches for Goal-directed and Stimulus-driven Driving Tasks.

The automotive industry is rapidly developing new in-vehicle technologies that can provide drivers with information to aid awareness and promote quicker response times. Particularly, vehicles with augmented reality (AR) graphics delivered via head-up displays (HUDs) are nearing mainstream commercial feasibility and will be widely implemented over the next decade. Though AR graphics have been shown to provide tangible benefits to drivers in scenarios like forward collision warnings and navigation, they also create many new perceptual and sensory issues for drivers. For some time now, designers have focused on increasing the realism and quality of virtual graphics delivered via HUDs, and recently have begun testing more advanced 3D HUD systems that deliver volumetric spatial information to drivers. However, the realization of volumetric graphics adds further complexity to the design and delivery of AR cues, and moreover, parameters in this new design space must be clearly and operationally defined and explored. In this work, we present two user studies that examine how driver performance and visual attention are affected when using fixed and animated AR HUD interface design approaches in driving scenarios that require top-down and bottom-up cognitive processing. Results demonstrate that animated design approaches can produce some driving gains (e.g., in goal-directed navigation tasks) but often come at the cost of response time and distance. Our discussion yields AR HUD design recommendations and challenges some of the existing assumptions of world-fixed conformal graphic approaches to design.

Driver Behavior and Performance with Augmented Reality Pedestrian Collision Warning: An Outdoor User Study.

This article investigates the effects of visual warning presentation methods on human performance in augmented reality (AR) driving. An experimental user study was conducted in a parking lot where participants drove a test vehicle while braking for any cross traffic with assistance from AR visual warnings presented on a monoscopic and volumetric head-up display (HUD). Results showed that monoscopic displays can be as effective as volumetric displays for human performance in AR braking tasks. The experiment also demonstrated the benefits of conformal graphics, which are tightly integrated into the real world, such as their ability to guide drivers' attention and their positive consequences on driver behavior and performance. These findings suggest that conformal graphics presented via monoscopic HUDs can enhance driver performance by leveraging the effectiveness of monocular depth cues. The proposed approaches and methods can be used and further developed by future researchers and practitioners to better understand driver performance in AR as well as inform usability evaluation of future automotive AR applications.

Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center.

The Pathosystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center (https://www.patricbrc.org). Recent changes to PATRIC include a redesign of the web interface and some new services that provide users with a platform that takes them from raw reads to an integrated analysis experience. The redesigned interface allows researchers direct access to tools and data, and the emphasis has changed to user-created genome-groups, with detailed summaries and views of the data that researchers have selected. Perhaps the biggest change has been the enhanced capability for researchers to analyze their private data and compare it to the available public data. Researchers can assemble their raw sequence reads and annotate the contigs using RASTtk. PATRIC also provides services for RNA-Seq, variation, model reconstruction and differential expression analysis, all delivered through an updated private workspace. Private data can be compared by 'virtual integration' to any of PATRIC's public data. The number of genomes available for comparison in PATRIC has expanded to over 80 000, with a special emphasis on genomes with antimicrobial resistance data. PATRIC uses this data to improve both subsystem annotation and k-mer classification, and tags new genomes as having signatures that indicate susceptibility or resistance to specific antibiotics.

The immune epitope database (IEDB) 3.0.

The IEDB, www.iedb.org, contains information on immune epitopes--the molecular targets of adaptive immune responses--curated from the published literature and submitted by National Institutes of Health funded epitope discovery efforts. From 2004 to 2012 the IEDB curation of journal articles published since 1960 has caught up to the present day, with >95% of relevant published literature manually curated amounting to more than 15,000 journal articles and more than 704,000 experiments to date. The revised curation target since 2012 has been to make recent research findings quickly available in the IEDB and thereby ensure that it continues to be an up-to-date resource. Having gathered a comprehensive dataset in the IEDB, a complete redesign of the query and reporting interface has been performed in the IEDB 3.0 release to improve how end users can access this information in an intuitive and biologically accurate manner. We here present this most recent release of the IEDB and describe the user testing procedures as well as the use of external ontologies that have enabled it.

Comparative genomic analysis at the PATRIC, a bioinformatic resource center.

The Pathosystems Resource Integration Center (PATRIC) is a genomics-centric relational database and bioinformatics resource designed to assist scientists in infectious-disease research. This method paper provides detailed instructions on using this resource to finding data specific to genomes, saving it in a personalized workspace and using a variety of interactive tools to analyze that data. While PATRIC contains many diverse tools and functionalities to explore both genome-scale and gene expression data, the main focus of this chapter is on comparative analysis of bacterial genomes.

PATRIC, the bacterial bioinformatics database and analysis resource.

The Pathosystems Resource Integration Center (PATRIC) is the all-bacterial Bioinformatics Resource Center (BRC) (http://www.patricbrc.org). A joint effort by two of the original National Institute of Allergy and Infectious Diseases-funded BRCs, PATRIC provides researchers with an online resource that stores and integrates a variety of data types [e.g. genomics, transcriptomics, protein-protein interactions (PPIs), three-dimensional protein structures and sequence typing data] and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes in PATRIC, currently more than 10,000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. All the data and integrated analysis and visualization tools are freely available. This manuscript describes updates to the PATRIC since its initial report in the 2007 NAR Database Issue.

PATRIC: the comprehensive bacterial bioinformatics resource with a focus on human pathogenic species.

Funded by the National Institute of Allergy and Infectious Diseases, the Pathosystems Resource Integration Center (PATRIC) is a genomics-centric relational database and bioinformatics resource designed to assist scientists in infectious-disease research. Specifically, PATRIC provides scientists with (i) a comprehensive bacterial genomics database, (ii) a plethora of associated data relevant to genomic analysis, and (iii) an extensive suite of computational tools and platforms for bioinformatics analysis. While the primary aim of PATRIC is to advance the knowledge underlying the biology of human pathogens, all publicly available genome-scale data for bacteria are compiled and continually updated, thereby enabling comparative analyses to reveal the basis for differences between infectious free-living and commensal species. Herein we summarize the major features available at PATRIC, dividing the resources into two major categories: (i) organisms, genomes, and comparative genomics and (ii) recurrent integration of community-derived associated data. Additionally, we present two experimental designs typical of bacterial genomics research and report on the execution of both projects using only PATRIC data and tools. These applications encompass a broad range of the data and analysis tools available, illustrating practical uses of PATRIC for the biologist. Finally, a summary of PATRIC's outreach activities, collaborative endeavors, and future research directions is provided.