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.

翻译：针对大型语言模型（LLM）在自动驾驶中的应用，已有多种方法被提出。一种利用LLM实现自动驾驶的策略是将周围物体及其坐标、速度信息以文本提示形式输入LLM，随后输出车辆的下一个运动指令。在此类应用中，空间识别与规划能力至关重要。具体而言，需要具备两项基础能力：（1）空间感知决策能力——即从坐标信息中识别空间关系并做出避碰决策；（2）遵守交通规则的能力。然而，目前尚未有研究量化评估不同类型LLM处理这些问题的准确程度。本研究在自动驾驶背景下，对LLM的上述两项能力进行了定量评估。此外，为验证这些能力在实际车辆中部署的可行性，我们开发了一套基于LLM驱动的车辆系统。