接收、推理与反应：在自动驾驶车辆中借助大型语言模型按您所说驾驶
Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles
AI总结：
本文提出利用大型语言模型（LLMs）增强自动驾驶决策的框架，通过思维链提示和口头反馈实现个性化驾驶，实验证明其提升决策质量并支持透明、自适应的自动驾驶系统。
摘要：
以人为本的设计与人工智能（AI）能力的融合，为超越交通范畴的下一代自动驾驶车辆开辟了新的可能性。这些车辆能够动态地与乘客互动，并适应其偏好。本文提出了一种新颖框架，利用大型语言模型（LLMs）来增强自动驾驶车辆中的决策过程。通过利用LLMs的语言和上下文理解能力以及专门工具，我们旨在将LLMs的语言和推理能力集成到自动驾驶车辆中。我们的研究包括在HighwayEnv（一个用于自动驾驶和战术决策任务的环境集合）中进行实验，以探索LLMs在各种场景下的解释、交互和推理能力。我们还考察了实时个性化，展示了LLMs如何基于口头命令影响驾驶行为。我们的实证结果凸显了使用思维链提示的显著优势，带来了改进的驾驶决策，并展示了LLMs通过持续口头反馈增强个性化驾驶体验的潜力。所提出的框架旨在转变自动驾驶车辆的操作，提供个性化支持、透明的决策和持续学习，以增强安全性和有效性。我们实现了以用户为中心、透明且自适应的自动驾驶生态系统，这得益于将LLMs集成到自动驾驶车辆中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
