Augmented Reality for Future Mobility: Insights from a Literature Review and HCI Workshop

Riegler, Andreas; Riener, Andreas; Holzmann, Clemens

doi:10.1515/icom-2021-0029

Cited by 23 publications

(8 citation statements)

References 224 publications

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“…Artificial intelligence (AI) is becoming more commonly used in many applications especially autonomous driving (AD) ones such as augmented reality (AR) [ 14 ], automatic emergency braking (AEB) [ 15 ], lane keeping assist (LKA) [ 16 ], active cruise control (ACC) [ 17 ], and crash avoidance (CA) [ 18 ]. Forward collision warning (FCW) [ 19 ] and automatic emergency braking (AEB) are considered as the initial trials to integrate crash avoidance functionality.…”

Section: Literature Reviewmentioning

confidence: 99%

SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System

Abdou¹,

Kamal

2022

Sensors

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Currently, deep learning and IoT collaboration is heavily invading automotive applications especially in autonomous driving throughout successful assistance functionalities. Crash avoidance, path planning, and automatic emergency braking are essential functionalities for autonomous driving. Trigger-action-based IoT platforms are widely used due to its simplicity and ability of doing receptive tasks accurately. In this work, we propose SDC-Net system: an end-to-end deep learning IoT hybrid system in which a multitask neural network is trained based on different input representations from a camera-cocoon setup installed in CARLA simulator. We build our benchmark dataset covering different scenarios and corner cases that the vehicle may expose in order to navigate safely and robustly while testing. The proposed system aims to output relevant control actions for crash avoidance, path planning and automatic emergency braking. Multitask learning with a bird’s eye view input representation outperforms the nearest representation in precision, recall, f1-score, accuracy, and average MSE by more than 11.62%, 9.43%, 10.53%, 6%, and 25.84%, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System

Abdou¹,

Kamal

2022

Sensors

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“…Those require at least 60° by 40° (see [2]). This is as well the motivation to search for alternatives to HUD for in-car augmentation (overview see [3]); Figure 1 provides an overview of approaches for augmented reality in cars including brief statements on merits and shortcomings:…”

Section: Introductionmentioning

confidence: 99%

RGB LED matrix display for augmented driving for higher traffic safety

Blankenbach¹,

Eisenhardt

Brezing³

et al. 2023

Advances in Display Technologies XIII

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Augmentation in manually driven vehicles can raise traffic safety significantly. The most ergonomic (eyes-on-the-road, no refocusing) solution is AR-HUD but the FOV is limited today to 10° by 5°. Transparent displays in the windshield (eyes-on-the-road, refocusing required) are costly (replacement) and hardly meet legal requirements for transparency. The cheapest solution is video-AR on dashboard displays (eyes-off-the-road, refocusing required). We report on a new approach for augmentation as compromise between ergonomics and cost: An eight-line RGB matrix display to be mounted on top of the dashboard at the bottom of the windshield. It spreads from pillar-to-pillar (150 cm, 150 x 8 pixel, RGB LED) and therefore enables augmented information along the whole windshield. In consequence, it needs less eyes-off-the road and refocusing and is a very ergonomic add-on for video-AR. We started with a single line pixelated light guide in a seating buck to measure and to evaluate the required luminance (⪆3,300 cd/m²), RGB luminance ratio (35:50:15) and perception of information from night to blinding sunlight. We optimized the RGB LED display by testing and measuring various diffusers at different distances to the LEDs for an optimum combination of sharpness and pixelation. Image quality and content such as the visualization of actual speed (including color-coding), warnings (e.g. slippery), navigation, and comfort functions (e.g. incoming call, beat mode) were evaluated by subjects via online survey and in our seating buck. The display was rated as being very helpful with significantly reduction in time for grasping the information.

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“…In [39], the concept of parallel ITS, consisting of a real and an artificial ITS, was explored. Other researchers have also discussed the prospects of AR for future mobility [40]. This paper serves as the first attempt to offer a review of the potential applications of Metaverse in the transportation industry.…”

Section: Introductionmentioning

confidence: 99%

Prospects and challenges of Metaverse application in data‐driven intelligent transportation systems

Njoku

Nwakanma

Amaizu

et al. 2022

IET Intelligent Trans Sys

122

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The Metaverse is a concept used to refer to a virtual world that exists in parallel to the physical world. It has grown from a conceptual level to having real applications in virtual reality games. The applicability of the Metaverse in numerous sectors like marketing, education, social, and even advertising exists. However, there exists little or no work on Metaverse applicability to the transportation industry. Data‐driven intelligent transportation systems (DDITS) aim to provide more intelligent systems based on exploiting data. This paper reviews the concepts and features of the Metaverse. Also, the review goes over three dominant DDITS challenges: vehicle fault detection and repair, testing new technologies, and anti‐theft systems. In addition, it highlights prospective Metaverse solutions that apply to the DDITS. Buttressing the utility of Metaverse in DDITS, this paper presents two major case studies: the invisible to visible (I2V) and the Metaverse on Wheels (MoW) technologies. Finally, the influence, limitations, and open issues of Metaverse applications to DDITS are discussed.

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Augmented Reality for Future Mobility: Insights from a Literature Review and HCI Workshop

Cited by 23 publications

References 224 publications

SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System

SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System

RGB LED matrix display for augmented driving for higher traffic safety

Prospects and challenges of Metaverse application in data‐driven intelligent transportation systems

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