eprintid: 10189684
rev_number: 12
eprint_status: archive
userid: 699
dir: disk0/10/18/96/84
datestamp: 2024-03-26 12:00:26
lastmod: 2024-07-03 14:50:45
status_changed: 2024-03-26 12:00:26
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Wang, Xue-Fang
creators_name: Chen, Wen-Hua
creators_name: Jiang, Jingjing
creators_name: Yan, Yunda
title: High-level decision making for autonomous overtaking: An MPC-based switching control approach
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: MPC-based decision-making, optimal control,
switched system, autonomous overtaking.
note: © 2024 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
abstract: The key motivation of this paper lies in the development of a high-level decision-making framework for autonomous overtaking maneuvers on two-lane country roads with dynamic oncoming traffic. To generate an optimal and safe decision sequence for such scenario, an innovative high-level decision-making framework that combines model predictive control (MPC) and switching control methodologies is introduced. Specifically, the autonomous vehicle is abstracted and modelled as a switched system. This abstraction allows vehicle to operate in different modes corresponding to different high-level decisions. It establishes a crucial connection between high-level decision-making and low-level behaviour of the autonomous vehicle. Furthermore, barrier functions and predictive models that account for the relationship between the autonomous vehicle and oncoming traffic are incorporated. This technique enables us to guarantee the satisfaction of constraints, while also assessing performance within a prediction horizon. By repeatedly solving the online constrained optimization problems, we not only generate an optimal decision sequence for overtaking safely and efficiently but also enhance the adaptability and robustness. This adaptability allows the system to respond effectively to potential changes and unexpected events. Finally, the performance of the proposed MPC framework is demonstrated via simulations of four driving scenarios, which shows that it can handle multiple behaviours.
date: 2024-07
date_type: published
publisher: Wiley
official_url: https://doi.org/10.1049/itr2.12507
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2262175
doi: 10.1049/itr2.12507
lyricists_name: Yan, Yunda
lyricists_id: YYAAC15
actors_name: Yan, Yunda
actors_id: YYAAC15
actors_role: owner
full_text_status: public
publication: IET Intelligent Transport Systems
volume: 18
number: 7
pagerange: 1259-1271
issn: 1751-956X
citation:        Wang, Xue-Fang;    Chen, Wen-Hua;    Jiang, Jingjing;    Yan, Yunda;      (2024)    High-level decision making for autonomous overtaking: An MPC-based switching control approach.                   IET Intelligent Transport Systems , 18  (7)   pp. 1259-1271.    10.1049/itr2.12507 <https://doi.org/10.1049/itr2.12507>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10189684/7/Yan_High-level%20decision%20making%20for%20autonomous%20overtaking_VoR.pdf