“We present Agent-Driver, an LLM-powered agent that revolutionizes the traditional perception-prediction-planning framework, establishing a powerful yet flexible paradigm for human-like autonomous driving.<p>Agent-Driver integrates a tool library for dynamic perception and prediction, a cognitive memory for human knowledge, and a reasoning engine that emulates human decision-making, all orchestrated by LLMs to enable a more anthropomorphic autonomous driving process.<p>Agent-Driver significantly outperforms the state-of-the-art autonomous driving systems by a large margin, with over 30% collision improvements in motion planning. Our approach also demonstrates strong few-shot learning ability and interpretability on the nuScenes benchmark.”