Empowering Autonomous Driving with Large Language Models: A Safety Perspective

arXiv:2312.00812v4 Announce Type: replace-cross
Abstract: Autonomous Driving (AD) encounters significant safety hurdles in long-tail unforeseen driving scenarios, largely stemming from the non-interpretability and poor generalization of the deep neural networks within the AD system, particularly in out-of-distribution and uncertain data. To this end, this paper explores the integration of Large Language Models (LLMs) into AD systems, leveraging their robust common-sense knowledge and reasoning abilities. The proposed methodologies employ LLMs as intelligent decision-makers in behavioral planning, augmented with a safety verifier …

abstract arxiv autonomous autonomous driving cs.ai cs.lg cs.sy data distribution driving eess.sy integration interpretability language language models large language large language models networks neural networks paper perspective safety stemming type uncertain