  Various methods have been proposed for utilizing Large Language Models (LLMs)
in autonomous driving. One strategy of using LLMs for autonomous driving
involves inputting surrounding objects as text prompts to the LLMs, along with
their coordinate and velocity information, and then outputting the subsequent
movements of the vehicle. When using LLMs for such purposes, capabilities such
as spatial recognition and planning are essential. In particular, two
foundational capabilities are required: (1) spatial-aware decision making,
which is the ability to recognize space from coordinate information and make
decisions to avoid collisions, and (2) the ability to adhere to traffic rules.
However, quantitative research has not been conducted on how accurately
different types of LLMs can handle these problems. In this study, we
quantitatively evaluated these two abilities of LLMs in the context of
autonomous driving. Furthermore, to conduct a Proof of Concept (POC) for the
feasibility of implementing these abilities in actual vehicles, we developed a
system that uses LLMs to drive a vehicle.

Computer Vision and Pattern Recognition;Computation and Language;Robotics