Double Lane Change Path Planning Using Reinforcement Learning with Field Tests
Date
Language
en
Reading access rights:
Open access
Rights Holder
Budapest University of Technology and Economics
Conference Date
2022.03.31
Conference Place
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
ISBN, e-ISBN
ISBN 978-963-421-873-9
Container Title
Proceedings of The First Conference on ZalaZONE Related R&I Activities of Budapest University of Technology and Economics 2022
Department
Department of Automotive Technologies
Version
Kiadói változat
Faculty
Faculty of Transportation Engineering and Vehicle Engineering
First Page
67
Subject (OSZKAR)
Local path planning
Model predictive control
Reinforcement learning
Vehicle dynamics
Model predictive control
Reinforcement learning
Vehicle dynamics
University
Budapest University of Technology and Economics
- Cite this item
- https://doi.org/10.3311/BMEZalaZONE2022-014
OOC works
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