Energy-Efficient Autonomous Vehicle Control Using Reinforcement Learning and Interactive Traffic Simulations

Huayi Li, Nan Li, Ilya Kolmanovsky, Anouck Girard

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


Connected and autonomous vehicles are expected to improve mobility and transportation, as well as to provide energy efficiency benefits. The integration of safety and energy efficiency aspects is challenging as there are certain tradeoffs between them, and also because the assessment of these attributes requires different time horizons. This paper illustrates the development of a controller for highway driving that, through reinforcement learning, can simultaneously address requirements of safety, comfort, performance and energy efficiency for battery electric vehicles. The training process of the decision policy exploits traffic simulations that are capable of representing the interactive behavior of vehicles in traffic based on game theory. Results indicate the potential for improved energy efficiency by adding powertrain-related states in the decision policy and by suitably defining the reward function.

Original languageEnglish
Title of host publication2020 American Control Conference, ACC 2020
Number of pages6
ISBN (Electronic)9781538682661
StatePublished - Jul 2020
Event2020 American Control Conference, ACC 2020 - Denver, United States
Duration: Jul 1 2020Jul 3 2020

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2020 American Control Conference, ACC 2020
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2020 AACC.

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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