Driving Style Alignment for LLM-powered Driver Agent
This addresses the challenge of creating autonomous driving agents with diverse, human-like behaviors for improved safety and user acceptance, though it appears incremental in building on existing LLM-agent research.
The paper tackles the problem of aligning LLM-powered driver agents with human driving styles by proposing a multi-alignment framework using demonstrations and feedback, validated through CARLA simulations and human evaluations.
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.