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.

翻译：人本设计与人工智能能力的融合为超越传统交通的下一代自动驾驶车辆开辟了新可能。这类车辆能够动态与乘客互动，并适应其偏好。本文提出一种利用大型语言模型增强自动驾驶车辆决策过程的新框架。通过将大型语言模型的语言理解与语境分析能力结合专用工具，我们致力于将语言与推理能力集成至自动驾驶系统。研究在高速公路环境（HighwayEnv）——一个面向自动驾驶与战术决策任务的环境集合——中开展实验，探索大型语言模型在多种场景下的语义解析、交互与推理能力。我们同时检验实时个性化功能，证明大型语言模型如何根据语音指令影响驾驶行为。实证结果凸显了链式思维提示的显著优势：它不仅优化驾驶决策，更展示了大型语言模型通过持续语音反馈提升个性化驾驶体验的潜力。该框架旨在变革自动驾驶车辆运作模式，通过个性化支持、透明决策与持续学习提升安全性与效能。最终，我们构建了以用户为核心、透明且自适应的自动驾驶生态系统，其核心支撑正是大型语言模型与自动驾驶车辆的深度融合。