LoDriver: A Region-Localized Autonomous Driver Using Large Language Models

Driving autonomously in diverse environments remains a significant challenge, especially when transitioning between regions with distinct traffic cultures and regulations. While human drivers exhibit remarkable adaptability through experiential learning and cognitive modeling, current data-driven autonomous systems often struggle with environmental adaptation, interpretability, and continuous learning capabilities. In this work, we present LoDriver (Local Driver), a novel knowledge-based architecture that enhances the conventional scene understanding-decision-planning paradigm through cognitive-inspired dual-process reasoning for path planning. LoDriver integrates a reactive process and a deliberative mechanism, processing multi-modal scene descriptions through parallel pathways. It maintains a structured memory module that dynamically accumulates driving experiences, traffic regulations, and knowledge, enabling experience-based decision-making and continuous learning through systematic memory updates. Experimental evaluation on the nuScenes dataset demonstrates LoDriver's interpretability and enhanced performance compared to existing knowledge-driven models, highlighting its advantage in operating across different environments.