# Smart Infrastructure: A Research Junction

arXiv: https://arxiv.org/abs/2307.06177 · HTML: https://mlsanity.com/paper/2307.06177/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 12 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble](https://mlsanity.com/paper/1803.03487/) (2018)
- [Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence](https://mlsanity.com/paper/1809.03916/) (2018)
- [Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure](https://mlsanity.com/paper/1803.02096/) (2018)

## Built on by (most cited first, 1 of 1)

- [DD-MDN: Human Trajectory Forecasting with Diffusion-Based Dual Mixture Density Networks and Uncertainty Self-Calibration](https://mlsanity.com/paper/2602.11214/) (2026)

## Abstract (arXiv)

Complex inner-city junctions are among the most critical traffic areas for injury and fatal accidents. The development of highly automated driving (HAD) systems struggles with the complex and hectic everyday life within those areas. Sensor-equipped smart infrastructures, which can communicate and cooperate with vehicles, are essential to enable a holistic scene understanding to resolve occlusions drivers and vehicle perception systems for themselves can not cover. We introduce an intelligent research infrastructure equipped with visual sensor technology, located at a public inner-city junction in Aschaffenburg, Germany. A multiple-view camera system monitors the traffic situation to perceive road users' behavior. Both motorized and non-motorized traffic is considered. The system is used for research in data generation, evaluating new HAD sensors systems, algorithms, and Artificial Intelligence (AI) training strategies using real-, synthetic- and augmented data. In addition, the junction features a highly accurate digital twin. Real-world data can be taken into the digital twin for simulation purposes and synthetic data generation.