Empowering Autonomous Driving with Large Language Models: A Safety Perspective

Autor:	Wang, Yixuan, Jiao, Ruochen, Zhan, Sinong Simon, Lang, Chengtian, Huang, Chao, Wang, Zhaoran, Yang, Zhuoran, Zhu, Qi
Rok vydání:	2023
Předmět:	Computer Science - Artificial Intelligence Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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 shield for contextual safety learning, for enhancing driving performance and safety. We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine. Demonstrating superior performance and safety metrics compared to state-of-the-art approaches, our approach shows the promising potential for using LLMs for autonomous vehicles. Comment: Accepted to LLMAgent workshop @ICLR2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.00812 Zobrazit plný text záznamu View this record from Arxiv