# Demonstration of a Time-Efficient Mobility System Using a Scaled Smart City

arXiv: https://arxiv.org/abs/1903.01632 · HTML: https://mlsanity.com/paper/1903.01632/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways](https://mlsanity.com/paper/2109.11672/) (2021)
- [Autonomous Driving Small-Scale Cars: A Survey of Recent Development](https://mlsanity.com/paper/2404.06229/) (2024)
- [Exiting the Simulation: The Road to Robust and Resilient Autonomous Vehicles at Scale](https://mlsanity.com/paper/2210.10876/) (2022)

## Abstract (arXiv)

The implementation of connected and automated vehicle (CAV) technologies enables a novel computational framework to deliver real-time control actions that optimize travel time, energy, and safety. Hardware is an integral part of any practical implementation of CAVs, and as such, it should be incorporated in any validation method. However, high costs associated with full scale, field testing of CAVs have proven to be a significant barrier. In this paper, we present the implementation of a decentralized control framework, which was developed previously, in a scaled-city using robotic CAVs, and discuss the implications of CAVs on travel time. Supplemental information and videos can be found at https://sites.google.com/view/ud-ids-lab/tfms.