Risk-Adaptive Edge--Cloud Visual Reasoning for Communication-Efficient Autonomous Driving

[2608.14991] Risk-Adaptive Edge--Cloud Visual Reasoning for Communication-Efficient Autonomous Driving Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2608.14991 (cs) [Submitted on 15 Aug 2026] Title: Risk-Adaptive Edge--Cloud Visual Reasoning for Communication-Efficient Autonomous Driving Authors: Meng Ma , Shuyang Li , Naigang Wang , Ruimin Ke View a PDF of the paper titled Risk-Adaptive Edge--Cloud Visual Reasoning for Communication-Efficient Autonomous Driving, by Meng Ma and 3 other authors View PDF HTML (experimental) Abstract: Cloud-hosted vision-language models (VLMs) offer greater contextual reasoning capabilities than smaller onboard models, but frequent visual uploads increase communication overhead and add network and inference latency to tactical decisions. We present a risk-adaptive edge-cloud architecture in which onboard traffic assessment determines when cloud reasoning is requested. An onboard VLM and a lightweight detector capture temporal traffic conditions and path-relative hazards for conservative local response and selective cloud access. The cloud model provides tactical advice, while validation, vehicle control, and automatic emergency braking remain local. In CARLA experiments, our method matched the task success rate of periodic cloud access while reducing cloud requests by 54.1% and recording fewer automatic emergency braking (AEB) activations. In a delayed-roadwork ablation, semantic events triggered requests before the next scheduled audit. Across three emulated network profiles, the method continued to reduce cloud traffic, although lane changes took longer than with periodic access. Onboard traffic assessment therefore served as a practical trigger for selective VLM inference in these experiments. Comments: 7 pages, 4 figures, 5 tables Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.14991 [cs.CV] (or arXiv:2608.14991v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14991 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Meng Ma [ view email ] [v1] Sat, 15 Aug 2026 02:36:01 UTC (1,349 KB) Full-text links: Access Paper: View a PDF of the paper titled Risk-Adaptive Edge--Cloud Visual Reasoning for Communication-Efficient Autonomous Driving, by Meng Ma and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Risk-Adaptive Edge--Cloud Visual Reasoning for Communication-Efficient Autonomous Driving#612427
• Efficient Representations for Autonomous Driving#246750
• AnchorReasoning: A Visual Grounding and Causal Reasoning Dataset in Long-Tail Autonomous Driving Scenarios#1064969
• Grounding Vision-Language Models in Driving Semantics: A Multi-Dataset Predicate Framework for Explainable Reasoning#1115592
• On Asymmetric Optimization of Reasoning and Perception in Vision-Language Model Post-Training#821884