In addressing these challenges, we found the major

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(a) Conventional Perception-Prediction-Planning Pipeline.PerceptionPredictionPlanning(b) Agent-Driver: LLMs as an Agent for Autonomous Driving.Neural ModulesTool LibraryReasoning EngineAgent-DriverCognitive MemorySensoryInputsActionActionSensoryInputsFigure 2. Architecture of Agent-Driver. Our system dynamically collects necessary environmental information from the output of neural
modules via the tool library. The collected information is further utilized to query the cognitive memory. Consequently, the reasoning
engine takes collected environmental information and retrieved memory data as input, and traceably derives a safe and comfortable trajectory
for driving through chain-of-thought reasoning, task planning, motion planning, and self-reflection.
ALanguageAgentforAutonomousDriving