Abstract Augmented reality (AR) devices, as smart glasses, enable users to see both the real world and virtual images simultaneously, contributing to an immersive experience in interactions and visualization. Recently, to reduce the size and weight of smart glasses, waveguides incorporating holographic optical elements in the form of advanced grating structures have been utilized to provide light-weight solutions instead of bulky helmet-type headsets. However current waveguide displays often have limited display resolution, efficiency and field-of-view, with complex multi-step fabrication processes of lower yield. In addition, current AR displays often have vergence-accommodation conflict in the augmented and virtual images, resulting in focusing-visual fatigue and eye strain. Here we report metasurface optical elements designed and experimentally implemented as a platform solution to overcome these limitations. Through careful dispersion control in the excited propagation and diffraction modes, we design and implement our high-resolution full-color prototype, via the combination of analytical–numerical simulations, nanofabrication and device measurements. With the metasurface control of the light propagation, our prototype device achieves a 1080-pixel resolution, a field-of-view more than 40°, an overall input–output efficiency more than 1%, and addresses the vergence-accommodation conflict through our focal-free implementation. Furthermore, our AR waveguide is achieved in a single metasurface-waveguide layer, aiding the scalability and process yield control.
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DeResolver: A Decentralized Conflict Resolution Framework with Autonomous Negotiation for Smart City Services
As various smart services are increasingly deployed in modern cities, many unexpected conflicts arise due to various physical world couplings. Existing solutions for conflict resolution often rely on centralized control to enforce predetermined and fixed priorities of different services, which is challenging due to the inconsistent and private objectives of the services. Also, the centralized solutions miss opportunities to more effectively resolve conflicts according to their spatiotemporal locality of the conflicts. To address this issue, we design a decentralized negotiation and conflict resolution framework named DeResolver, which allows services to resolve conflicts by communicating and negotiating with each other to reach a Pareto-optimal agreement autonomously and efficiently. Our design features a two-step self-supervised learning-based algorithm to predict acceptable proposals and their rankings of each opponent through the negotiation. Our design is evaluated with a smart city case study of three services: intelligent traffic light control, pedestrian service, and environmental control. In this case study, a data-driven evaluation is conducted using a large dataset consisting of the GPS locations of 246 surveillance cameras and an automatic traffic monitoring system with more than 3 million records per day to extract real-world vehicle routes. The evaluation results show that our solution achieves much more balanced results, i.e., only increasing the average waiting time of vehicles, the measurement metric of intelligent traffic light control service, by 6.8% while reducing the weighted sum of air pollutant emission, measured for environment control service, by 12.1%, and the pedestrian waiting time, the measurement metric of pedestrian service, by 33.1%, compared to priority-based solution.
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- Award ID(s):
- 1952096
- NSF-PAR ID:
- 10464847
- Date Published:
- Journal Name:
- ACM Transactions on Cyber-Physical Systems
- Volume:
- 6
- Issue:
- 4
- ISSN:
- 2378-962X
- Page Range / eLocation ID:
- 1 to 27
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3529096
