Risk Occupancy: A New and Efficient Paradigm through Vehicle-Road-Cloud Collaboration

[2408.07367] Risk Occupancy: A New and Efficient Paradigm through Vehicle-Road-Cloud Collaboration Skip to main content Search Submit Donate Log in Computer Science > Robotics arXiv:2408.07367 (cs) [Submitted on 14 Aug 2024 ( v1 ), last revised 4 Aug 2026 (this version, v3)] Title: Risk Occupancy: A New and Efficient Paradigm through Vehicle-Road-Cloud Collaboration Authors: Jiaxing Chen , Wei Zhong , Bolin Gao , Yifei Liu , Hengduo Zou , Jiaxi Liu , Yanbo Lu , Jin Huang , Zhihua Zhong View PDF HTML (experimental) Abstract: This paper proposes a novel 4D risk occupancy (RiskOcc) perception paradigm under the Vehicle-Road-Cloud integrated architecture, which unifies object detection and local mapping into a single representation spanning four dimensions: road-surface x and y coordinates, risk, and time. Distinct from conventional grid occupancy and risk field methods, this paradigm adopts an anchor-node-based perception approach with a concise yet reliable risk quantification scheme, enabling flexible and accurate capture of static and dynamic object occupancy states at current and future time steps, with per-occupancy-unit risk quantification. Compared with 3D-Occ, the proposed RiskOcc requires only one layer of data volume and achieves higher perceptual computational efficiency. Visualizations of risk occupancy perception results are presented based on the DAIR-V2X dataset, and a path planning method is developed to verify the usability of the risk occupancy map. Experimental results show that at an initial braking speed of 8 m/s, the model improves safety redundancy by 12.5\% and reduces average deceleration by 5.41\%, enhancing both safety and comfort. In summary, this study introduces a novel perception paradigm for intelligent connected vehicle (ICV), providing a compact and interpretable risk representation for cooperative perception and downstream planning in ICV systems. Comments: 11 pages,10 figures Subjects: Robotics (cs.RO) Cite as: arXiv:2408.07367 [cs.RO] (or arXiv:2408.07367v3 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2408.07367 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IEEE Transactions on Intelligent Transportation Systems, Early Access, 2026 Related DOI : https://doi.org/10.1109/TITS.2026.3707600 Focus to learn more DOI(s) linking to related resources Submission history From: Jiaxing Chen [ view email ] [v1] Wed, 14 Aug 2024 08:26:50 UTC (16,889 KB) [v2] Sat, 17 Aug 2024 04:18:59 UTC (16,889 KB) [v3] Tue, 4 Aug 2026 07:52:22 UTC (19,986 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.RO < prev | next > new | recent | 2024-08 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation 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)

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