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Exploring Large Language Models for Trajectory Prediction: A Technical Perspective
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A4 Artikkeli konferenssijulkaisussa
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en
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5
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HRI 2024 Companion - Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, pp. 774-778, ACM/IEEE International Conference on Human-Robot Interaction
Abstract
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.
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Publisher Copyright: © 2024 Copyright held by the owner/author(s)
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Munir, F, Mihaylova, T, Azam, S, Kucner, T P & Kyrki, V 2024, Exploring Large Language Models for Trajectory Prediction: A Technical Perspective. in HRI 2024 Companion - Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. ACM/IEEE International Conference on Human-Robot Interaction, IEEE, pp. 774-778, ACM/IEEE International Conference on Human-Robot Interaction, Boulder, Colorado, United States, 11/03/2024. https://doi.org/10.1145/3610978.3640625