Driving Style Alignment for LLM-powered Driver Agent

2 code implementations • 17 Mar 2024 • Ruoxuan Yang, Xinyue Zhang, Anais Fernandez-Laaksonen, Xin Ding, Jiangtao Gong

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.

Autonomous Driving Decision Making