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.

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