Demonstration of a Time-Efficient Mobility System Using a Scaled Smart City
Not on the map. Why?
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Mar 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1.
3 papers.
- 2021A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadwayscited by 6
- 2024Autonomous Driving Small-Scale Cars: A Survey of Recent Developmentcited by 6
- 2022Exiting the Simulation: The Road to Robust and Resilient Autonomous Vehicles at Scalecited by 0
- 2024Autonomous Driving Small-Scale Cars: A Survey of Recent Development9 Apr 2024
- 2022Exiting the Simulation: The Road to Robust and Resilient Autonomous Vehicles at Scale19 Oct 2022
- 2021A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways23 Sep 2021