@inproceedings{e7b1b8f7e2a646ab925223885e9dcf5b,
title = "Reinforcement Learning Enabled Safety-Critical Tracking of Automated Vehicles with Uncertainties via Integrated Control-Dependent, Time-Varying Barrier Function, and Control Lyapunov Function",
abstract = "Model uncertainties are considered in a learning-based control framework that combines control dependent barrier function (CDBF), time-varying control barrier function (TCBF), and control Lyapunov function (CLF). Tracking control is achieved by CLF, while safety-critical constraints during tracking are guaranteed by CDBF and TCBF. A reinforcement learning (RL) method is applied to jointly learn model uncertainties that related to CDBF, TCBF, and CLF. The learning-based framework eventually formulates a quadratic programming (QP) with different constraints of CDBF, TCBF and CLF involving model uncertainties. It is the first time to apply the proposed learning-based framework for safety-guaranteed tracking control of automated vehicles with uncertainties. The control performances are validated for two different single-lane change maneuvers via Simulink/CarSim{\textregistered} co-simulation and compared for the cases with and without learning. Moreover, the learning effects are discussed through explainable constraints in the QP formulation.",
keywords = "Automated vehicles, control-dependent barrier function, reinforcement learning, safety-critical tracking, uncertainties",
author = "Jingxiong Meng and Junfeng Zhao and Yan Chen",
note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors.; 3rd Modeling, Estimation and Control Conference, MECC 2023 ; Conference date: 02-10-2023 Through 05-10-2023",
year = "2023",
month = oct,
day = "1",
doi = "10.1016/j.ifacol.2023.12.005",
language = "English (US)",
series = "IFAC-PapersOnLine",
publisher = "Elsevier B.V.",
number = "3",
pages = "85--90",
editor = "Marcello Canova",
booktitle = "IFAC-PapersOnLine",
edition = "3",
}