  The applications of Vision-Language Models (VLMs) in the field of Autonomous
Driving (AD) have attracted widespread attention due to their outstanding
performance and the ability to leverage Large Language Models (LLMs). By
incorporating language data, driving systems can gain a better understanding of
real-world environments, thereby enhancing driving safety and efficiency. In
this work, we present a comprehensive and systematic survey of the advances in
vision language models in this domain, encompassing perception and
understanding, navigation and planning, decision-making and control, end-to-end
autonomous driving, and data generation. We introduce the mainstream VLM tasks
in AD and the commonly utilized metrics. Additionally, we review current
studies and applications in various areas and summarize the existing
language-enhanced autonomous driving datasets thoroughly. Lastly, we discuss
the benefits and challenges of VLMs in AD and provide researchers with the
current research gaps and future trends.
