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Title:

Trajectory Planning Using Reinforcement Learning for Interactive Overtaking Maneuvers in Autonomous Racing Scenarios

Document type:
Konferenzbeitrag
Contribution type:
Textbeitrag / Aufsatz
Author(s):
Ögretmen, L.; Chen, M.; Pitschi, P.; Lohmann, B.
Abstract:
Conventional trajectory planning approaches for autonomous racing are based on the sequential execution of prediction of the opposing vehicles and subsequent trajectory planning for the ego vehicle. If the opposing vehicles do not react to the ego vehicle, they can be predicted accurately. However, if there is interaction between the vehicles, the prediction loses its validity. For high interaction, instead of a planning approach that reacts exclusively to the fixed prediction, a trajectory plan...     »
Keywords:
trajectory planning; reinforcement learning; overtaking maneuvers; autonmomous racing scenarios
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Editor:
IEEE
Book / Congress title:
IEEE International Conference on Intelligent Transportation Systems (ITSC) [27´th]
Organization:
IEEE
Date of congress:
24.-27.9.2024
Publisher:
IEEE
Publisher address:
New York
Year:
2024
Year / month:
2024-09
Month:
Sep
Reviewed:
ja
Language:
en
Publication format:
Print
WWW:
https://arxiv.org/abs/2404.10658
Semester:
SS 24
TUM Institution:
Lehrstuhl für Regelungstechnik
Ingested:
31.07.2024
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