Vehicle-Infrastructure Collaborative Perception (VICP) is pivotal for resolving occlusion in autonomous driving, yet the trade-off between communication bandwidth and feature redundancy remains a critical bottleneck. While intermediate fusion mitigates data volume compared to raw sharing, existing frameworks typically rely on spatial compression or static confidence maps, which inefficiently transmit spatially redundant features from non-critical background regions. To address this, we propose Risk-intent Selective detection (RiSe), an interaction-aware framework that shifts the paradigm from identifying visible regions to prioritizing risk-critical ones. Specifically, we introduce a Potential Field-Trajectory Correlation Model (PTCM) grounded in potential field theory to quantitatively assess kinematic risks. Complementing this, an Intention-Driven Area Prediction Module (IDAPM) leverages ego-motion priors to proactively predict and filter key Bird's-Eye-View (BEV) areas essential for decision-making. By integrating these components, RiSe implements a semantic-selective fusion scheme that transmits high-fidelity features only from high-interaction regions, effectively acting as a feature denoiser. Extensive experiments on the DeepAccident dataset demonstrate that our method reduces communication volume to 0.71\% of full feature sharing while maintaining state-of-the-art detection accuracy, establishing a competitive Pareto frontier between bandwidth efficiency and perception performance.

翻译：车路协同感知对于解决自动驾驶中的遮挡问题至关重要，然而通信带宽与特征冗余之间的权衡仍是一个关键瓶颈。虽然相较于原始数据共享，中间层融合减少了数据量，但现有框架通常依赖于空间压缩或静态置信度图，这些方法会低效地传输来自非关键背景区域的空间冗余特征。为解决这一问题，我们提出了风险意图选择性检测框架，这是一个交互感知的框架，将范式从识别可见区域转向优先处理风险关键区域。具体而言，我们引入了一个基于势场理论的势场-轨迹关联模型，用于定量评估运动学风险。作为补充，一个意图驱动的区域预测模块利用自车运动先验信息，主动预测并筛选对决策至关重要的鸟瞰图关键区域。通过整合这些组件，RiSe实现了一种语义选择性融合方案，仅从高交互区域传输高保真特征，有效地充当了特征去噪器。在DeepAccident数据集上进行的大量实验表明，我们的方法将通信量降低至全特征共享的0.71%，同时保持了最先进的检测精度，在带宽效率与感知性能之间建立了具有竞争力的帕累托前沿。