  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.
