Fuel Consumption Reduction of Multi-Lane Road Networks using Decentralized Mixed-Autonomy Control

Nathan Lichtle, Eugene Vinitsky, George Gunter, Akash Velu, Alexandre M. Bayen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work, we demonstrate optimization of fuel economy in a large, calibrated model of a portion of the Ventura Freeway using a low penetration rate of controlled autonomous vehicles. We create waves in this network using a string-unstable car-following model and introduce a ghost cell to allow waves to propagate out of the network. Using multi-agent reinforcement learning, we then design a controller that manages to partially dampen the waves and thus increase the average energy efficiency of the system, yielding a 25% fuel consumption reduction at a 10% penetration rate. Finally, we investigate the robustness properties of the designed controller. We find that the controller regulates the system to its equilibrium speed over a wide range of speeds and penetrations outside the training set, indicating generalization and robustness.

Original languageEnglish (US)
Title of host publication2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2068-2073
Number of pages6
ISBN (Electronic)9781728191423
DOIs
StatePublished - Sep 19 2021
Event2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021 - Indianapolis, United States
Duration: Sep 19 2021Sep 22 2021

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume2021-September

Conference

Conference2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
Country/TerritoryUnited States
CityIndianapolis
Period9/19/219/22/21

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Fuel Consumption Reduction of Multi-Lane Road Networks using Decentralized Mixed-Autonomy Control'. Together they form a unique fingerprint.

Cite this