Double Lane Change Path Planning Using Reinforcement Learning with Field Tests

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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