自主驾驶中用于决策的大型语言模型评估

摘要

已经提出了各种方法来利用大型语言模型（LLMs）进行自动驾驶。一种使用LLMs进行自动驾驶的策略涉及将周围物体作为文本提示输入到LLMs中，同时提供它们的坐标和速度信息，然后输出车辆的后续移动。在利用LLMs进行这种目的时，空间识别和规划等能力是至关重要的。特别是，需要两个基础能力：（1）空间感知决策制定，即从坐标信息中识别空间并做出避免碰撞的决策，以及（2）遵守交通规则的能力。然而，目前尚未对不同类型的LLMs如何准确处理这些问题进行定量研究。在本研究中，我们定量评估了LLMs在自动驾驶背景下的这两种能力。此外，为了对在实际车辆中实现这些能力的可行性进行概念验证，我们开发了一个利用LLMs驾驶车辆的系统。

English

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.

自主驾驶中用于决策的大型语言模型评估

Evaluation of Large Language Models for Decision Making in Autonomous Driving

摘要

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