Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Models

arXiv:2405.04909v1 Announce Type: new
Abstract: Predicting the future trajectories of dynamic traffic actors is a cornerstone task in autonomous driving. Though existing notable efforts have resulted in impressive performance improvements, a gap persists in scene cognitive and understanding of the complex traffic semantics. This paper proposes Traj-LLM, the first to investigate the potential of using Large Language Models (LLMs) without explicit prompt engineering to generate future motion from agents' past/observed trajectories and scene semantics. Traj-LLM starts with sparse context joint …

abstract actors arxiv autonomous autonomous driving cognitive cs.ai cs.cv driving dynamic exploration future gap improvements language language models large language large language models llm paper performance prediction semantics traffic trajectory type understanding