Exploring Large Language Models for Trajectory Prediction: A Technical Perspective

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

48 Lataukset (Pure)

Abstrakti

Large Language Models (LLMs) have been recently proposed for trajectory prediction in autonomous driving, where they potentially can provide explainable reasoning capability about driving situations. Most studies use versions of the OpenAI GPT, while there are open-source alternatives which have not been evaluated in this context. In this report1, we study their trajectory prediction performance as well as their ability to reason about the situation. Our results indicate that open-source alternatives are feasible for trajectory prediction. However, their ability to describe situations and reason about potential consequences of actions appears limited, and warrants future research.

AlkuperäiskieliEnglanti
OtsikkoHRI 2024 Companion - Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
KustantajaIEEE
Sivut774-778
Sivumäärä5
ISBN (elektroninen)979-8-4007-0323-2
DOI - pysyväislinkit
TilaJulkaistu - 11 maalisk. 2024
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaACM/IEEE International Conference on Human-Robot Interaction - Boulder, Yhdysvallat
Kesto: 11 maalisk. 202415 maalisk. 2024

Julkaisusarja

NimiACM/IEEE International Conference on Human-Robot Interaction
ISSN (elektroninen)2167-2148

Conference

ConferenceACM/IEEE International Conference on Human-Robot Interaction
LyhennettäHRI
Maa/AlueYhdysvallat
KaupunkiBoulder
Ajanjakso11/03/202415/03/2024

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