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 | ||
AbstractPerforming 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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