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