TY - GEN
T1 - Exploring Large Language Models for Trajectory Prediction: A Technical Perspective
AU - Munir, Farzeen
AU - Mihaylova, Tsvetomila
AU - Azam, Shoaib
AU - Kucner, Tomasz Piotr
AU - Kyrki, Ville
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s)
PY - 2024/3/11
Y1 - 2024/3/11
N2 - 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.
AB - 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.
KW - Autonomous Driving
KW - Large Language Models
KW - Trajectory Prediction
UR - http://www.scopus.com/inward/record.url?scp=85188135375&partnerID=8YFLogxK
U2 - 10.1145/3610978.3640625
DO - 10.1145/3610978.3640625
M3 - Conference article in proceedings
AN - SCOPUS:85188135375
T3 - ACM/IEEE International Conference on Human-Robot Interaction
SP - 774
EP - 778
BT - HRI 2024 Companion - Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
PB - IEEE
T2 - ACM/IEEE International Conference on Human-Robot Interaction
Y2 - 11 March 2024 through 15 March 2024
ER -