Műegyetemi Digitális Archívum

Double Lane Change Path Planning Using Reinforcement Learning with Field Tests

Fehér, Árpád
Aradi, Szilárd
Bécsi, Tamás
2022-05-03T10:04:48Z
2022-05-03T10:04:48Z
2022

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

http://hdl.handle.net/10890/16978
en
Double Lane Change Path Planning Using Reinforcement Learning with Field Tests
Open access
Budapest University of Technology and Economics
2022.03.31
Budapest University of Technology and Economics
The First Conference on ZalaZONE Related R&I Activities of Budapest University of Technology and Economics 2022
2022.03.31
ISBN 978-963-421-873-9
Budapest University of Technology and Economics
Budapest
Proceedings of The First Conference on ZalaZONE Related R&I Activities of Budapest University of Technology and Economics 2022
Department of Automotive Technologies
Kiadói változat
Faculty of Transportation Engineering and Vehicle Engineering
67
10.3311/BMEZalaZONE2022-014
70
Local path planning
Model predictive control
Reinforcement learning
Vehicle dynamics
Budapest University of Technology and Economics

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