[2605.08133] VLADriver-RAG: Retrieval-Augmented Vision-Language-Action Models for Autonomous Driving
[Submitted on 1 May 2026]
Title:VLADriver-RAG: Retrieval-Augmented Vision-Language-Action Models for Autonomous Driving
View a PDF of the paper titled VLADriver-RAG: Retrieval-Augmented Vision-Language-Action Models for Autonomous Driving, by Rui Zhao and 3 other authors
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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.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.08133 [cs.CV]
(or arXiv:2605.08133v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.08133
arXiv-issued DOI via DataCite
Submission history
From: Zhao Rui [view email] [v1] Fri, 1 May 2026 05:50:00 UTC (17,787 KB)
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