According to the World Health Organization, the involvement of Vulnerable Road Users (VRUs) in traffic accidents remains a significant concern, with VRUs accounting for over half of traffic fatalities. The increase of automation and connectivity levels of vehicles has still an uncertain impact on VRU safety. By deploying the Collective Perception Service (CPS), vehicles can include information about VRUs in Vehicle-to-Everything (V2X) messages, thus raising the general perception of the environment. Although an increased awareness is considered positive, one could argue that the awareness ratio, the metric used to measure perception, is only implicitly connected to the VRUs' safety. This paper introduces a tailored metric, the Risk Factor (RF), to measure the risk level for the interactions between Connected Automated Vehicles (CAVs) and VRUs. By evaluating the RF, we assess the impact of V2X communication on VRU risk mitigation. Our results show that high V2X penetration rates can reduce mean risk, quantified by our proposed metric, by up to 44%. Although the median risk value shows a significant decrease, suggesting a reduction in overall risk, the distribution of risk values reveals that CPS's mitigation effectiveness is overestimated, which is indicated by the divergence between RF and awareness ratio. Additionally, by analyzing a real-world traffic dataset, we pinpoint high-risk locations within a scenario, identifying areas near intersections and behind parked cars as especially dangerous. Our methodology can be ported and applied to other scenarios in order to identify high-risk areas. We value the proposed RF as an insightful metric for quantifying VRU safety in a highly automated and connected environment.

翻译：据世界卫生组织统计，弱势道路使用者（VRU）在交通事故中的占比超过半数，其安全问题仍是重大关切。车辆自动化与网联化水平的提升对VRU安全性的影响仍存在不确定性。通过部署集体感知服务（CPS），车辆可将VRU信息纳入车联网（V2X）消息中，从而提升对环境的整体感知能力。尽管增强环境感知被视为积极举措，但衡量感知能力的指标——感知率——与VRU安全的关联性仍较为间接。本文提出定制化度量指标——风险因子（RF），用于衡量联网自动驾驶车辆（CAV）与VRU交互中的风险水平。通过评估RF，我们量化V2X通信对降低VRU风险的影响。研究结果表明，高V2X渗透率可使平均风险（以所提指标量化）降低高达44%。虽然风险中位数显著下降表明整体风险降低，但风险值分布显示CPS的缓解效果被高估——这一现象通过RF与感知率之间的差异得以揭示。进一步基于真实交通数据集分析，我们定位出场景中的高风险区域，发现交叉口附近及停放车辆后方尤为危险。该方法可移植至其他场景以识别高风险区域。我们提出的RF作为量化高度自动化与网联化环境中VRU安全性的有效指标，具有重要价值。