言うがままに運転せよ：自動運転車における大規模言語モデルの活用
Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles
大規模言語モデル（LLM）を自動運転車の意思決定に組み込み、乗客の音声指示に基づく運転のパーソナライズや対話的な意思決定を実現するフレームワークを提案し、HighwayEnvでの実験で有効性を示した。
著者: Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Ziran Wang
分類: cs.HC, cs.AI, cs.RO
原文アブストラクト
The fusion of human-centric design and artificial intelligence (AI) capabilities has opened up new possibilities for next-generation autonomous vehicles that go beyond transportation. These vehicles can dynamically interact with passengers and adapt to their preferences. This paper proposes a novel framework that leverages Large Language Models (LLMs) to enhance the decision-making process in autonomous vehicles. By utilizing LLMs' linguistic and contextual understanding abilities with specialized tools, we aim to integrate the language and reasoning capabilities of LLMs into autonomous vehicles. Our research includes experiments in HighwayEnv, a collection of environments for autonomous driving and tactical decision-making tasks, to explore LLMs' interpretation, interaction, and reasoning in various scenarios. We also examine real-time personalization, demonstrating how LLMs can influence driving behaviors based on verbal commands. Our empirical results highlight the substantial advantages of utilizing chain-of-thought prompting, leading to improved driving decisions, and showing the potential for LLMs to enhance personalized driving experiences through ongoing verbal feedback. The proposed framework aims to transform autonomous vehicle operations, offering personalized support, transparent decision-making, and continuous learning to enhance safety and effectiveness. We achieve user-centric, transparent, and adaptive autonomous driving ecosystems supported by the integration of LLMs into autonomous vehicles.
