SGVLM: Depth-Integrated Semantic Scene Graph Fusion for Enhanced Autonomous Driving Decision-Making

SGVLM: Depth-Integrated Semantic Scene Graph Fusion for Enhanced Autonomous Driving Decision-Making | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Computational Intelligence This is a preprint and has not been peer reviewed. Data may be preliminary. 13 September 2025 V1 Latest version Share on SGVLM: Depth-Integrated Semantic Scene Graph Fusion for Enhanced Autonomous Driving Decision-Making Authors : Yiming Han2 , Yiran Tao and Xiang Cui , and Tinglun Song [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175774544.49992943/v1 Published Computational Intelligence Version of record Peer review timeline 314 views 151 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Autonomous driving decision-making requires a deep semantic understanding of traffic scenes. In this paper, we propose the SGVLM (Semantic Graph Vision-Language Model) architecture: a vision-language model that enhances autonomous driving decision-making through depth-integrated semantic scene graph fusion. Key objects are represented as nodes (category, state) and spatial-semantic relations as edges, enriched with pixel-wise depth estimates from Depth-Anything-V2 to capture accurate inter-object distances. These structured graph features are aggregated via a two-layer Graph Attention Network and projected into the FastVLM’s FastViTHD feature space. A cross-modal triplet fusion layer then jointly integrates graph embeddings, visual features, and natural-language queries. Leveraging Low-Rank Adaptation (LoRA) for efficient fine-tuning, SGVLM_7B achieves relative improvements of 25.9% in BLEU-4 and 18.6% in ROUGE-L over the InternVL4Drive-v2 baseline on the DriveLM-nuScenes benchmark, and attains 94.56% accuracy on collision-warning decision tasks in our TTSG-data safety-critical scenarios. These results demonstrate that depth-integrated semantic scene graph fusion substantially enhances the model’s ability to generate actionable driving decisions under complex traffic conditions. Supplementary Material File (sgvlm.docx) Download 5.33 MB Information & Authors Information Version history V1 Version 1 13 September 2025 Peer review timeline Published Computational Intelligence Version of Record 13 Apr 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Computational Intelligence Keywords autonomous driving graph attention network semantic scene graph Authors Affiliations Yiming Han2 Nanjing University of Aeronautics and Astronautics College of Energy and Power Engineering View all articles by this author Yiran Tao and Xiang Cui Nanjing University of Aeronautics and Astronautics College of Energy and Power Engineering View all articles by this author Tinglun Song [email protected] Chery Automobile Co Ltd View all articles by this author Metrics & Citations Metrics Article Usage 314 views 151 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yiming Han2, Yiran Tao and Xiang Cui, Tinglun Song. SGVLM: Depth-Integrated Semantic Scene Graph Fusion for Enhanced Autonomous Driving Decision-Making. Authorea . 13 September 2025. DOI: https://doi.org/10.22541/au.175774544.49992943/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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