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Double Lane Change Path Planning Using Reinforcement Learning with Field Tests

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Link to refer to this document:
10.3311/BMEZalaZONE2022-014
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  • Conference on BME ZalaZONE 2022 [22]
Abstract
Performing dynamic double lane-change maneuvers can be a challenge for highly automated vehicles. The algorithm must meet safety requirements while keeping the vehicle stable and controllable. The problem of path planning is numerically complex and must be run at a high refresh rate. The article presents a new approach to avoiding obstacles for autonomous vehicles. To solve this problem
Title
Double Lane Change Path Planning Using Reinforcement Learning with Field Tests
Author
Fehér, Árpád
Aradi, Szilárd
Bécsi, Tamás
Date of issue
2022
Access level
Open access
Copyright owner
Budapest University of Technology and Economics
Conference title
The First Conference on ZalaZONE Related R&I Activities of Budapest University of Technology and Economics 2022
Conference place
Budapest University of Technology and Economics
Conference date
2022.03.31
Language
en
Page
67 - 70
Subject
Local path planning, Model predictive control, Reinforcement learning, Vehicle dynamics
Version
Kiadói változat
Identifiers
DOI: 10.3311/BMEZalaZONE2022-014
Title of the container document
Proceedings of The First Conference on ZalaZONE Related R&I Activities of Budapest University of Technology and Economics 2022
ISBN, e-ISBN
ISBN 978-963-421-873-9
University
Budapest University of Technology and Economics
Faculty
Faculty of Transportation Engineering and Vehicle Engineering
Department
Department of Automotive Technologies

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