# Smart City Intersections: Intelligence Nodes for Future Metropolises

arXiv: https://arxiv.org/abs/2205.01686 · HTML: https://mlsanity.com/paper/2205.01686/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 7 of 7)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [Cluster Contrast for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2103.11568/) (2021)
- [Birds Eye View Social Distancing Analysis System](https://mlsanity.com/paper/2112.07159/) (2021)

## Built on by (most cited first, 2 of 2)

- [Boundless: Generating Photorealistic Synthetic Data for Object Detection in Urban Streetscapes](https://mlsanity.com/paper/2409.03022/) (2024)
- [SOD-YOLOv8 -- Enhancing YOLOv8 for Small Object Detection in Traffic Scenes](https://mlsanity.com/paper/2408.04786/) (2024)

## Abstract (arXiv)

Traffic intersections are the most suitable locations for the deployment of computing, communications, and intelligence services for smart cities of the future. The abundance of data to be collected and processed, in combination with privacy and security concerns, motivates the use of the edge-computing paradigm which aligns well with physical intersections in metropolises. This paper focuses on high-bandwidth, low-latency applications, and in that context it describes: (i) system design considerations for smart city intersection intelligence nodes; (ii) key technological components including sensors, networking, edge computing, low latency design, and AI-based intelligence; and (iii) applications such as privacy preservation, cloud-connected vehicles, a real-time "radar-screen", traffic management, and monitoring of pedestrian behavior during pandemics. The results of the experimental studies performed on the COSMOS testbed located in New York City are illustrated. Future challenges in designing human-centered smart city intersections are summarized.