Logic-RAG is a novel framework that improves how autonomous vehicles understand spatial relationships in road scenes by augmenting large multimodal models with visual-spatial knowledge.

• Converts complex visual scenes into first-order logic representations
• Creates dynamic knowledge bases of object-object relationships
• Significantly improves spatial reasoning capabilities in driving scenarios
• Enhances system interpretability and user trust in autonomous systems

This research addresses a critical gap in autonomous driving technology by enabling AI systems to better understand and reason about the spatial relationships between objects on the road—a fundamental requirement for safe and reliable autonomous driving systems.

Logic-RAG: Augmenting Large Multimodal Models with Visual-Spatial Knowledge for Road Scene Understanding