  Autonomous driving technology, a catalyst for revolutionizing transportation
and urban mobility, has the tend to transition from rule-based systems to
data-driven strategies. Traditional module-based systems are constrained by
cumulative errors among cascaded modules and inflexible pre-set rules. In
contrast, end-to-end autonomous driving systems have the potential to avoid
error accumulation due to their fully data-driven training process, although
they often lack transparency due to their "black box" nature, complicating the
validation and traceability of decisions. Recently, large language models
(LLMs) have demonstrated abilities including understanding context, logical
reasoning, and generating answers. A natural thought is to utilize these
abilities to empower autonomous driving. By combining LLM with foundation
vision models, it could open the door to open-world understanding, reasoning,
and few-shot learning, which current autonomous driving systems are lacking. In
this paper, we systematically review a research line about \textit{Large
Language Models for Autonomous Driving (LLM4AD)}. This study evaluates the
current state of technological advancements, distinctly outlining the principal
challenges and prospective directions for the field. For the convenience of
researchers in academia and industry, we provide real-time updates on the
latest advances in the field as well as relevant open-source resources via the
designated link: https://github.com/Thinklab-SJTU/Awesome-LLM4AD.
