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

• URL: http://arxiv.org/abs/2503.12663v1
• Date: Sun, 16 Mar 2025 21:36:36 GMT
• Title: Logic-RAG: Augmenting Large Multimodal Models with Visual-Spatial Knowledge for Road Scene Understanding
• Authors: Imran Kabir, Md Alimoor Reza, Syed Billah,
• Abstract summary: Large multimodal models (LMMs) are increasingly integrated into autonomous driving systems for user interaction.<n>However, their limitations in fine-grained spatial reasoning pose challenges for system interpretability and user trust.<n>We introduce Logic-RAG, a novel Retrieval-Augmented Generation framework that improves LMMs' spatial understanding in driving scenarios.
• Score: 0.5571177307684636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large multimodal models (LMMs) are increasingly integrated into autonomous driving systems for user interaction. However, their limitations in fine-grained spatial reasoning pose challenges for system interpretability and user trust. We introduce Logic-RAG, a novel Retrieval-Augmented Generation (RAG) framework that improves LMMs' spatial understanding in driving scenarios. Logic-RAG constructs a dynamic knowledge base (KB) about object-object relationships in first-order logic (FOL) using a perception module, a query-to-logic embedder, and a logical inference engine. We evaluated Logic-RAG on visual-spatial queries using both synthetic and real-world driving videos. When using popular LMMs (GPT-4V, Claude 3.5) as proxies for an autonomous driving system, these models achieved only 55% accuracy on synthetic driving scenes and under 75% on real-world driving scenes. Augmenting them with Logic-RAG increased their accuracies to over 80% and 90%, respectively. An ablation study showed that even without logical inference, the fact-based context constructed by Logic-RAG alone improved accuracy by 15%. Logic-RAG is extensible: it allows seamless replacement of individual components with improved versions and enables domain experts to compose new knowledge in both FOL and natural language. In sum, Logic-RAG addresses critical spatial reasoning deficiencies in LMMs for autonomous driving applications. Code and data are available at https://github.com/Imran2205/LogicRAG.

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