VLP: Vision Language Planning for Autonomous Driving

arXiv:2401.05577v3 Announce Type: replace
Abstract: Autonomous driving is a complex and challenging task that aims at safe motion planning through scene understanding and reasoning. While vision-only autonomous driving methods have recently achieved notable performance, through enhanced scene understanding, several key issues, including lack of reasoning, low generalization performance and long-tail scenarios, still need to be addressed. In this paper, we present VLP, a novel Vision-Language-Planning framework that exploits language models to bridge the gap between linguistic understanding and autonomous driving. …

abstract arxiv autonomous autonomous driving cs.cv driving key language low motion planning performance planning reasoning through type understanding vision