Predictive cruise control of connected and autonomous vehicles via reinforcement learning

Weinan Gao, Adedapo Odekunle, Yunfeng Chen, Zhong Ping Jiang

Research output: Contribution to journalArticle

Abstract

Predictive cruise control concerns designing controllers for autonomous vehicles using the broadcasted information from the traffic lights such that the idle time around the intersection can be reduced. This study proposes a novel adaptive optimal control approach based on reinforcement learning to solve the predictive cruise control problem of a platoon of connected and autonomous vehicles. First, the reference velocity is determined for each autonomous vehicle in the platoon. Second, a data-driven adaptive optimal control algorithm is developed to estimate the gains of the desired distributed optimal controllers without the exact knowledge of system dynamics. The obtained controller is able to regulate the headway, velocity, and acceleration of each vehicle in a suboptimal sense. The goal of trip time reduction is achieved without compromising vehicle safety and passenger comfort. Numerical simulations are presented to validate the efficacy of the proposed methodology.

Original languageEnglish (US)
Pages (from-to)2849-2855
Number of pages7
JournalIET Control Theory and Applications
Volume13
Issue number17
DOIs
StatePublished - Nov 26 2019

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Control and Optimization
  • Electrical and Electronic Engineering

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