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Titel:

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

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
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...     »
Stichworte:
trajectory planning; reinforcement learning; overtaking maneuvers; autonmomous racing scenarios
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Herausgeber:
IEEE
Kongress- / Buchtitel:
IEEE International Conference on Intelligent Transportation Systems (ITSC) [27´th]
Ausrichter der Konferenz:
IEEE
Datum der Konferenz:
24.-27.9.2024
Verlag / Institution:
IEEE
Verlagsort:
New York
Jahr:
2024
Jahr / Monat:
2024-09
Monat:
Sep
Reviewed:
ja
Sprache:
en
Erscheinungsform:
Print
WWW:
https://arxiv.org/abs/2404.10658
Semester:
SS 24
TUM Einrichtung:
Lehrstuhl für Regelungstechnik
Eingabe:
31.07.2024
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