VLADriver-RAG: Retrieval-Augmented Vision-Language-Action Models for Autonomous Driving

• URL: http://arxiv.org/abs/2605.08133v2
• Date: Tue, 12 May 2026 14:57:35 GMT
• Title: VLADriver-RAG: Retrieval-Augmented Vision-Language-Action Models for Autonomous Driving
• Abstract summary: We propose textbfVLA-DriverRAG, a framework that grounds in explicit, structure-aware historical knowledge.<n>We abstract sensory inputs into semantic graphs via a textitVisual-to-Scenario mechanism, effectively filtering visual noise.<n>Experiments on the Bench2Drive benchmark establish a new state-of-the-art, achieving a Driving Score of 89.12.
• Score: 8.228888952282917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving, yet their reliance on implicit parametric knowledge limits generalization in long-tail scenarios. While Retrieval-Augmented Generation (RAG) offers a solution by accessing external expert priors, standard visual retrieval suffers from high latency and semantic ambiguity. To address these challenges, we propose \textbf{VLADriver-RAG}, a framework that grounds planning in explicit, structure-aware historical knowledge. Specifically, we abstract sensory inputs into spatiotemporal semantic graphs via a \textit{Visual-to-Scenario} mechanism, effectively filtering visual noise. To ensure retrieval relevance, we employ a \textit{Scenario-Aligned Embedding Model} that utilizes Graph-DTW metric alignment to prioritize intrinsic topological consistency over superficial visual similarity. These retrieved priors are then fused within a query-based VLA backbone to synthesize precise, disentangled trajectories. Extensive experiments on the Bench2Drive benchmark establish a new state-of-the-art, achieving a Driving Score of 89.12.

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