@inproceedings{18f28ef84ffb4bf895d3655a8f0dbb80,
title = "Personalized Vehicle Path Following Based on Robust Gain-scheduling Control in Lane-changing and Left-turning Maneuvers",
abstract = "Riding comfort significantly affects the acceptance of automated driving systems. Vehicle performance should align with drivers' preference to ensure the trust and usage of the automated driving functions. The challenge lies in matching individual humans' driving behaviors that vary from one driver to another. A personalized path-following control method is proposed in this paper to track the expected paths of an individual driver. The personalized path is generated via waypoints that are obtained from historical driving data of the respective driver. Then, a robust path-following controller is designed to track the personalized path. The H∞ performance and eigenvalue placement method are adopted to compute the feedback control gain. A gain-scheduling technique is employed to deal with the system time-varying parameters. CarSim{\textregistered} simulations based on a high-fidelity vehicle model are conducted to validate the proposed control method. Simulation results show the generated paths reflect drivers' preferences and can be tracked by the designed path-following controller.",
keywords = "automated driving, driver preferences, path-following control, personalized path generation",
author = "Yimin Chen and Junmin Wang",
note = "Publisher Copyright: {\textcopyright} 2018 AACC.; 2018 Annual American Control Conference, ACC 2018 ; Conference date: 27-06-2018 Through 29-06-2018",
year = "2018",
month = aug,
day = "9",
doi = "10.23919/ACC.2018.8431065",
language = "英语",
isbn = "9781538654286",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4784--4789",
booktitle = "2018 Annual American Control Conference, ACC 2018",
}