Open AccessPredictive cruise control of connected and autonomous vehicles via reinforcement learning
Author(s) -
Gao Weinan,
Odekunle Adedapo,
Chen Yunfeng,
Jiang ZhongPing
Publication year - 2019
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2018.6031
Subject(s) - cruise control , platoon , cooperative adaptive cruise control , headway , reinforcement learning , control theory (sociology) , acceleration , controller (irrigation) , model predictive control , intersection (aeronautics) , computer science , control engineering , control (management) , engineering , simulation , artificial intelligence , physics , classical mechanics , agronomy , biology , aerospace engineering
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.