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Fehér, Árpád
Aradi, Szilárd
Bécsi, Tamás
2022-05-03T10:04:48Z
2022-05-03T10:04:48Z
2022
http://hdl.handle.net/10890/16978
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
en
Double Lane Change Path Planning Using Reinforcement Learning with Field Tests
Kiadói változat
Open access
Budapest University of Technology and Economics
Faculty of Transportation Engineering and Vehicle Engineering
2022.03.31
Budapest University of Technology and Economics
10.3311/BMEZalaZONE2022-014
Department of Automotive Technologies
Budapest University of Technology and Economics
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
Local path planning
Model predictive control
Reinforcement learning
Vehicle dynamics
The First Conference on ZalaZONE Related R&I Activities of Budapest University of Technology and Economics 2022
67
70


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