Taking a step towards more human-like autonomous
driving, we propose Agent-Driver, a cognitive agent em-
powered by Large Language Models (LLMs). The fun-
damental insight of our approach lies in the utilization of
natural language as a unified interface, seamlessly bridging
language-based human knowledge and reasoning ability with
neural-network-based systems. Our approach fundamentally
transforms the conventional perception-prediction-planning
framework by leveraging LLMs as an interactive scheduler
among system components. As depicted in Figure 1 (b),
on top of the LLMs, we introduce: 1) a versatile tool
library that interfaces with neural modules via dynamic
function calls, streamlining perception with less redundancy,
ALanguageAgentforAutonomousDriving