Cooperative perception (CP) offers significant potential to overcome the limitations of single-vehicle sensing by enabling information sharing among connected vehicles (CVs). However, existing generic CP approaches need to transmit large volumes of perception data that are irrelevant to the driving safety, exceeding available communication bandwidth. Moreover, most CP frameworks rely on pre-defined communication partners, making them unsuitable for dynamic traffic environments. This paper proposes a Spontaneous Risk-Aware Selective Cooperative Perception (SRA-CP) framework to address these challenges. SRA-CP introduces a decentralized protocol where connected agents continuously broadcast lightweight perception coverage summaries and initiate targeted cooperation only when risk-relevant blind zones are detected. A perceptual risk identification module enables each CV to locally assess the impact of occlusions on its driving task and determine whether cooperation is necessary. When CP is triggered, the ego vehicle selects appropriate peers based on shared perception coverage and engages in selective information exchange through a fusion module that prioritizes safety-critical content and adapts to bandwidth constraints. We evaluate SRA-CP on a public dataset against several representative baselines. Results show that SRA-CP achieves less than 1% average precision (AP) loss for safety-critical objects compared to generic CP, while using only 20% of the communication bandwidth. Moreover, it improves the perception performance by 15% over existing selective CP methods that do not incorporate risk awareness.

翻译：协同感知（CP）通过实现联网车辆（CVs）间的信息共享，为克服单车感知的局限性提供了重要潜力。然而，现有的通用CP方法需要传输大量与驾驶安全无关的感知数据，超出了可用通信带宽。此外，大多数CP框架依赖于预定义的通信伙伴，使其难以适应动态交通环境。本文提出一种基于自发风险感知的选择性协同感知（SRA-CP）框架以应对这些挑战。SRA-CP引入了一种去中心化协议，其中联网智能体持续广播轻量级的感知覆盖范围摘要，并仅在检测到与风险相关的盲区时启动定向协作。通过感知风险识别模块，每辆联网车辆能够本地评估遮挡对其驾驶任务的影响，并判断是否需要协同。当CP被触发时，主车基于共享的感知覆盖范围选择合适的协作伙伴，并通过融合模块进行选择性信息交换；该模块优先处理安全关键内容并适应带宽限制。我们在公开数据集上评估SRA-CP，并与多种代表性基线方法进行比较。结果表明，相较于通用CP方法，SRA-CP对安全关键目标的平均精度（AP）损失小于1%，同时仅占用20%的通信带宽。此外，与未纳入风险感知的现有选择性CP方法相比，其感知性能提升了15%。