  With the emergence of Large Language Models (LLMs) and Vision Foundation
Models (VFMs), multimodal AI systems benefiting from large models have the
potential to equally perceive the real world, make decisions, and control tools
as humans. In recent months, LLMs have shown widespread attention in autonomous
driving and map systems. Despite its immense potential, there is still a lack
of a comprehensive understanding of key challenges, opportunities, and future
endeavors to apply in LLM driving systems. In this paper, we present a
systematic investigation in this field. We first introduce the background of
Multimodal Large Language Models (MLLMs), the multimodal models development
using LLMs, and the history of autonomous driving. Then, we overview existing
MLLM tools for driving, transportation, and map systems together with existing
datasets and benchmarks. Moreover, we summarized the works in The 1st WACV
Workshop on Large Language and Vision Models for Autonomous Driving (LLVM-AD),
which is the first workshop of its kind regarding LLMs in autonomous driving.
To further promote the development of this field, we also discuss several
important problems regarding using MLLMs in autonomous driving systems that
need to be solved by both academia and industry.
