March 14, 2024, 4:45 a.m. | Pranav Singh Chib, Pravendra Singh

cs.CV updates on

arXiv:2403.08032v1 Announce Type: new
Abstract: Accurate pedestrian trajectory prediction is crucial for various applications, and it requires a deep understanding of pedestrian motion patterns in dynamic environments. However, existing pedestrian trajectory prediction methods still need more exploration to fully leverage these motion patterns. This paper investigates the possibilities of using Large Language Models (LLMs) to improve pedestrian trajectory prediction tasks by inducing motion cues. We introduce LG-Traj, a novel approach incorporating LLMs to generate motion cues present in pedestrian past/observed …

abstract applications arxiv dynamic environments exploration however language language models large language large language models llm llms paper patterns pedestrian prediction trajectory type understanding

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