Model Predictive Path Planning Based on Artificial Potential Field and Its Application to Autonomous Lane Change
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lin, Pengfei | - |
dc.contributor.author | Choi, Woo Young | - |
dc.contributor.author | Lee, Seung-Hi | - |
dc.contributor.author | Chung, Chung Choo | - |
dc.date.accessioned | 2022-05-23T08:49:27Z | - |
dc.date.available | 2022-05-23T08:49:27Z | - |
dc.date.created | 2022-05-23 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 2093-7121 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/27984 | - |
dc.description.abstract | In this paper, we propose a vehicle lane change system using model predictive path planning (MPPP) based on the artificial potential field (APF) for speeding vehicles. It is shown that APF has high performance in real-time obstacle avoidance. However, it remains unpractical for self-driving cars because the point model used for the APF ignores the lateral vehicle dynamics for the lane-keeping system. To resolve the problem, this paper introduces a novel curve-fitting method combined with the APF applied to plan a drivable path for autonomous vehicles in the lane change action. The proposed system was validated through MATLAB/Simulink with the empirical kinematic model. The simulation results indicate that the model predictive path planning algorithm is highly effective in high-speed lane change scenarios to avoid dynamic obstacle vehicles. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.subject | VEHICLES | - |
dc.title | Model Predictive Path Planning Based on Artificial Potential Field and Its Application to Autonomous Lane Change | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Seung-Hi | - |
dc.identifier.wosid | 000681746000117 | - |
dc.identifier.bibliographicCitation | 2020 20TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS), pp.731 - 736 | - |
dc.relation.isPartOf | 2020 20TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS) | - |
dc.citation.title | 2020 20TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS) | - |
dc.citation.startPage | 731 | - |
dc.citation.endPage | 736 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 3 | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Robotics | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Robotics | - |
dc.subject.keywordPlus | VEHICLES | - |
dc.subject.keywordAuthor | Autonomous Vehicle | - |
dc.subject.keywordAuthor | Collision Avoidance | - |
dc.subject.keywordAuthor | Artificial Potential Field | - |
dc.subject.keywordAuthor | Lane Change | - |
dc.subject.keywordAuthor | Optimal Path Planning | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
94, Wausan-ro, Mapo-gu, Seoul, 04066, Korea02-320-1314
COPYRIGHT 2020 HONGIK UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.