Recently, LLM-powered driver agents have demonstrated considerable potential
in the field of autonomous driving, showcasing human-like reasoning and
decision-making abilities.However, current research on aligning driver agent
behaviors with human driving styles remains limited, partly due to the scarcity
of high-quality natural language data from human driving behaviors.To address
this research gap, we propose a multi-alignment framework designed to align
driver agents with human driving styles through demonstrations and feedback.
Notably, we construct a natural language dataset of human driver behaviors
through naturalistic driving experiments and post-driving interviews, offering
high-quality human demonstrations for LLM alignment. The framework's
effectiveness is validated through simulation experiments in the CARLA urban
traffic simulator and further corroborated by human evaluations. Our research
offers valuable insights into designing driving agents with diverse driving
styles.The implementation of the framework and details of the dataset can be
found at the link.

通过自然语言数据集和实验来对駕駛代理人行为与人类駕駛风格进行多重对齐，验证该框架能够为设计具有多种驾驶风格的驾驶代理人提供有价值的洞察。

LLM 强化驾驶员代理的驾驶风格对齐

Driving Style Alignment for LLM-powered Driver Agent

Building a natural language dataset requires caution since word semantics is
vulnerable to subtle text change or the definition of the annotated concept.
Such a tendency can be seen in generative tasks like question-answering and
dialogue generation and also in tasks that create a categorization-based
corpus, like topic classification or sentiment analysis. Open-domain
conversations involve two or more crowdworkers freely conversing about any
topic, and collecting such data is particularly difficult for two reasons: 1)
the dataset should be ``crafted" rather than ``obtained" due to privacy
concerns, and 2) paid creation of such dialogues may differ from how
crowdworkers behave in real-world settings. In this study, we tackle these
issues when creating a large-scale open-domain persona dialogue corpus, where
persona implies that the conversation is performed by several actors with a
fixed persona and user-side workers from an unspecified crowd.

本研究创建了一个大规模的开放式人设对话语料库，解决了创建自然语言数据集时面对的语义敏感性、隐私问题、数据采集难度等多重难题。