  Existing learning-based autonomous driving (AD) systems face challenges in
comprehending high-level information, generalizing to rare events, and
providing interpretability. To address these problems, this work employs Large
Language Models (LLMs) as a decision-making component for complex AD scenarios
that require human commonsense understanding. We devise cognitive pathways to
enable comprehensive reasoning with LLMs, and develop algorithms for
translating LLM decisions into actionable driving commands. Through this
approach, LLM decisions are seamlessly integrated with low-level controllers by
guided parameter matrix adaptation. Extensive experiments demonstrate that our
proposed method not only consistently surpasses baseline approaches in
single-vehicle tasks, but also helps handle complex driving behaviors even
multi-vehicle coordination, thanks to the commonsense reasoning capabilities of
LLMs. This paper presents an initial step toward leveraging LLMs as effective
decision-makers for intricate AD scenarios in terms of safety, efficiency,
generalizability, and interoperability. We aspire for it to serve as
inspiration for future research in this field. Project page:
https://sites.google.com/view/llm-mpc
