Feedback Control Algorithms for the Dissipation of Traffic Waves with Autonomous Vehicles

Maria Laura Delle Monache (), Thibault Liard (), Anaïs Rat (), Raphael Stern (), Rahul Bhadani (), Benjamin Seibold (), Jonathan Sprinkle (), Daniel B. Work () and Benedetto Piccoli ()
Additional contact information
Maria Laura Delle Monache: University of Grenoble Alpes, Inria, CNRS, Grenoble INP, GIPSA-Lab
Thibault Liard: University of Grenoble Alpes, Inria, CNRS, Grenoble INP, GIPSA-Lab
Anaïs Rat: University of Rutgers
Raphael Stern: Institute for Software Integrated Systems, Vanderbilt University
Rahul Bhadani: University of Arizona
Benjamin Seibold: Temple University
Jonathan Sprinkle: University of Arizona
Daniel B. Work: Institute for Software Integrated Systems, Vanderbilt University
Benedetto Piccoli: University of Rutgers

Chapter Chapter 12 in Computational Intelligence and Optimization Methods for Control Engineering, 2019, pp 275-299 from Springer

Abstract: Abstract This article considers the problem of traffic control in which an autonomous vehicle is used to regulate human-piloted traffic to dissipate stop-and-go traffic waves. We first investigated the controllability of well-known microscopic traffic flow models, namely, (i) the Bando model (also known as the optimal velocity model), (ii) the follow-the-leader model, and (iii) a combined optimal velocity follow-the-leader model. Based on the controllability results, we proposed three control strategies for an autonomous vehicle to stabilize the other, human-piloted traffics. We subsequently simulate the control effects on the microscopic models of human drivers in numerical experiments to quantify the potential benefits of the controllers. Based on the simulations, finally, we conduct a field experiment with 22 human drivers and a fully autonomous-capable vehicle, to assess the feasibility of autonomous vehicle-based traffic control on real human-piloted traffic. We show that both in simulation and in the field test that an autonomous vehicle is able to dampen waves generated by 22 cars, and that as a consequence, the total fuel consumption of all vehicles is reduced by up to 20%.

Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-25446-9_12

Ordering information: This item can be ordered from
http://www.springer.com/9783030254469

DOI: 10.1007/978-3-030-25446-9_12

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().