Development of lane-level guidance service in vehicle augmented reality system

Lee, Jeong-Woo; Yoon, Changrak; Kang, Jun-Gyu; Park, Byoung-Jun; Kim, Kyong-Ho

doi:10.1109/icact.2015.7224799

Cited by 21 publications

(12 citation statements)

References 5 publications

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“…To achieve this goal, Augmented Reality (AR) with Head-Up Displays (HUDs) has attracted attention in the field of automotive research. Such AR-HUD systems have been studied to supporting driver visual guidance or providing lane-level guidance services [44], [45], [46], [47]. One of our previous efforts trying to fusion vision and cloud data to display surrounding vehicles' lane-change probability will be briefly introduced in this section.…”

Section: Establishing Driver Visual Guidance For Improved Safety and Efficiencymentioning

confidence: 99%

A Review of UTDrive Studies: Learning Driver Behavior From Naturalistic Driving Data

Liu¹,

Hansen

2021

IEEE Open J. Intell. Transp. Syst.

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Intelligent vehicles and Advanced Driver Assistance Systems (ADAS) are being developed rapidly over the past few years. Many applications such as vehicle localization, environment perception, and path planning have shown promising potentialities. While there is great interest in migrating from complete human-controlled vehicles towards fully autonomous vehicles, it is natural that researchers spending more effort trying to understand the interaction between vehicles with various levels of automation in large-scale traffic scenarios. Next-generation vehicles are expected to have the capacity of evaluating driver conditions, vehicle capabilities, surrounding traffic contexts, and take advantage of such knowledge to ensure safe and efficient driving. Three general research questions are raised to achieve this goal, which are (i) how can we acquire sufficient data, (ii) how to evaluate and understand driving behavior, and (iii) how to deliver information effectively to drivers. In this article, we present a review of previous studies from the UTDrive project attempts to answer above questions.

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Section: Establishing Driver Visual Guidance For Improved Safety and Efficiencymentioning

confidence: 99%

A Review of UTDrive Studies: Learning Driver Behavior From Naturalistic Driving Data

Liu¹,

Hansen

2021

IEEE Open J. Intell. Transp. Syst.

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“…Subsequently, the famous AR R&D (Research and Development) company called Layer was established, and an augmentation content store was released in April 2010, which has since become a platform for companies in this field. 16 In Park et al, 17 an AR-based system was developed for showing cultural heritage; meanwhile, in Barile et al 18 and Lee et al, 19 the lane-level guidance and the drive service had paid attention to AR technology which would help drivers change lanes and navigate.…”

Section: Related Workmentioning

confidence: 99%

Novel individual location recommendation with mobile based on augmented reality

Shi

Wang

Wei

et al. 2016

International Journal of Distributed Sensor Networks

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Location-based services for mobile users provide a convenient means of filtering information based on current geographical position. However, the information in traditional location service systems is provided through an abstract metaphor that the user has to understand and translate into action. In this paper, a mobile augmented reality-based system for individual location recommendation is proposed. The system consists of the modules of user location, marker detection, three-dimensional (3D) display and location guide by combing augmented reality with navigation technology. Through such a system, the position and orientation information from any sensory source were pervasively extracted and used to enhance the association to the real world by combining video techniques and 3D-graphics in an augmented reality view. Experimental results demonstrate that the proposed system is both efficient and effective in helping people locate their position. Compared with the traditional Global Positioning Systems-based location services system, the proposed system can also enhance users' senses experience by providing users with a more intuitive, 3D, dynamic information display and sharing capabilities.

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“…Although the benefits of AR are being realized in emerging novel head-mounted applications (e.g., as delivered via Microsoft HoloLens and Magic Leap One), its commercial use is largely limited by these technical and perceptual challenges, particularly in the automotive industry where there are likely promising gains to user (i.e., driver) performance. Specifically, when delivered via in-vehicle HUDs, AR graphics can increase drivers' forward situational awareness and cognition while minimizing distraction [13,14]. AR HUDs allow drivers to attend to the display while ----------------• J.L.…”

Section: Introductionmentioning

confidence: 99%

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

Gabbard

Smith

Merenda

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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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.

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Development of lane-level guidance service in vehicle augmented reality system

Cited by 21 publications

References 5 publications

A Review of UTDrive Studies: Learning Driver Behavior From Naturalistic Driving Data

A Review of UTDrive Studies: Learning Driver Behavior From Naturalistic Driving Data

Novel individual location recommendation with mobile based on augmented reality

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

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