Driving Style Alignment for LLM-powered Driver Agent

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 1 and details of the dataset 2 can be found at the link.

Authors

• Ruoxuan Yang
• Xinyue Zhang
• Anais Fernandez-Laaksonen
• Xin Ding
• Jiangtao Gong

Keywords

• Natural languages
• Decision making
• Encoding
• Cognition
• Autonomous agents
• Complexity theory
• Driver behavior
• Interviews
• Autonomous vehicles
• Intelligent robots
• Human Behavior
• Natural Language
• Simulation Experiments
• Dataset Details
• Human Drivers
• Collision
• Short-term Memory
• Pedestrian
• Average Speed
• Video Clips
• Language Model
• Questions In The Questionnaire
• Alignment Method
• Lane Change
• Memory Unit
• Feedback Group
• Reasonable Question
• Average Vehicle Speed