Occlusions present a significant challenge for connected and automated vehicles, as they can obscure critical road users from perception systems. Traditional risk metrics often fail to capture the cumulative nature of these threats over time adequately. In this paper, we propose a novel and universal risk assessment metric, the Risk of Tracking Loss (RTL), which aggregates instantaneous risk intensity throughout occluded periods. This provides a holistic risk profile that encompasses both high-intensity, short-term threats and prolonged exposure. Utilizing diverse and high-fidelity real-world datasets, a large-scale statistical analysis is conducted to characterize occlusion risk and validate the effectiveness of the proposed metric. The metric is applied to evaluate different vehicle-to-everything (V2X) deployment strategies. Our study shows that full V2X penetration theoretically eliminates this risk, the reduction is highly nonlinear; a substantial statistical benefit requires a high penetration threshold of 75-90%. To overcome this limitation, we propose a novel asymmetric communication framework that allows even non-connected vehicles to receive warnings. Experimental results demonstrate that this paradigm achieves better risk mitigation performance. We found that our approach at 25% penetration outperforms the traditional symmetric model at 75%, and benefits saturate at only 50% penetration. This work provides a crucial risk assessment metric and a cost-effective, strategic roadmap for accelerating the safety benefits of V2X deployment.

翻译：遮挡对联网自动驾驶车辆构成重大挑战，因为它们可能使关键道路使用者从感知系统中消失。传统风险度量往往无法充分捕捉这些威胁随时间累积的特性。本文提出一种新颖且通用的风险评估指标——跟踪丢失风险（RTL），该指标通过聚合整个遮挡期间的瞬时风险强度，形成同时涵盖高强度短期威胁与长期暴露的整体风险画像。利用多样化高保真实世界数据集进行的大规模统计分析，系统刻画了遮挡风险特征并验证了所提指标的有效性。将该指标应用于评估不同车联网（V2X）部署策略的研究表明：虽然理论上完全普及V2X可消除此类风险，但风险降低呈现高度非线性特征——需要达到75-90%的高普及阈值才能获得显著的统计效益。为突破此局限，我们提出一种创新的非对称通信框架，使非联网车辆也能接收预警。实验结果表明，该范式能实现更优的风险缓解性能：在25%普及率下，本方法优于传统对称模型在75%普及率的表现，且效益在仅50%普及率时即趋于饱和。本研究不仅提供了关键的风险评估指标，更为加速实现V2X部署的安全效益提出了具有成本效益的战略路线图。