對於自動駕駛中的決策製定，大型語言模型的評估

Evaluation of Large Language Models for Decision Making in Autonomous Driving

December 11, 2023

作者: Kotaro Tanahashi, Yuichi Inoue, Yu Yamaguchi, Hidetatsu Yaginuma, Daiki Shiotsuka, Hiroyuki Shimatani, Kohei Iwamasa, Yoshiaki Inoue, Takafumi Yamaguchi, Koki Igari, Tsukasa Horinouchi, Kento Tokuhiro, Yugo Tokuchi, Shunsuke Aoki

cs.AI

摘要

已經提出了各種方法來利用大型語言模型（LLMs）進行自動駕駛。一種使用LLMs進行自動駕駛的策略涉及將周圍物體作為文本提示輸入到LLMs中，並附帶它們的坐標和速度信息，然後輸出車輛的後續移動。在將LLMs用於此類目的時，空間識別和規劃等能力至關重要。特別是，需要兩個基本能力：（1）空間感知決策，即從性坐標信息中識別空間並做出避免碰撞的決策的能力，以及（2）遵守交通規則的能力。然而，目前尚未對不同類型的LLMs如何準確處理這些問題進行定量研究。在本研究中，我們在自動駕駛的背景下定量評估了LLMs的這兩種能力。此外，為了對實際車輛實施這些能力的可行性進行概念驗證，我們開發了一個使用LLMs來駕駛車輛的系統。

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.