风险占用：一种通过车路云协同实现的新型高效范式
Risk Occupancy: A New and Efficient Paradigm through Vehicle-Road-Cloud Collaboration
AI总结：
该研究提出车路云协同下的四维风险占用感知范式，其效率优于3D-Occ，在初始制动速度8m/s时提升安全冗余、降低减速度，为智能网联汽车提供紧凑可解释的风险表示。
摘要：
本文提出了一种在车路云一体化架构下的新型四维风险占用（RiskOcc）感知范式，该范式将目标检测与局部地图统一为包含四个维度的单一表示：路面x和y坐标、风险值以及时间。与传统的网格占用和风险场方法不同，该范式采用基于锚节点的感知方法，结合简洁且可靠的风险量化方案，能够灵活且准确地捕获当前及未来时间步的静态和动态目标占用状态，并实现每个占用单元的风险量化。与3D-Occ相比，所提出的RiskOcc仅需一层数据量，且实现了更高的感知计算效率。基于DAIR-V2X数据集展示了风险占用感知结果的可视化，并开发了一种路径规划方法以验证风险占用图的可用性。实验结果表明，在初始制动速度为8 m/s时，该模型将安全冗余度提高了12.5%，平均减速度降低了5.41%，同时提升了安全性与舒适性。综上所述，本研究为智能网联汽车（ICV）引入了一种新型感知范式，为ICV系统中的协同感知及下游规划提供了紧凑且可解释的风险表示。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
