Vulnerable road users (VRUs) account for approximately half of urban traffic deaths globally, with intersections concentrating a disproportionate share of these casualties. Recent reviews of sensing technology for VRU protection have cataloged dozens of single-sensor and dual-sensor deployments, yet none of the surveyed systems couples multi-modal sensing with edge-side near-miss analytics and bidirectional vehicle-to-everything (V2X) and pedestrian-to-everything (P2X) messaging in a single intersection cabinet. This paper presents an integrated framework for VRU protection at signalized intersections, combining LiDAR, radar, RGB camera, and thermal camera at the perception layer, edge-based prediction and surrogate-safety analytics at the computation layer, V2X and P2X messaging at the communication layer, and adaptive signal control at the actuation layer. The framework is grounded in an empirical case study using R-LiViT, the first publicly released roadside LiDAR-Visual-Thermal dataset, which provides 200 multi-modal sequences and 2,400 annotated RGB-T frames at three German intersections. Analysis of 53,319 detection annotations reveals that VRUs comprise approximately 49% of all road-user observations, that day-to-night density drops by 38% for pedestrians and 45% for vehicles while the night distribution shows a higher close-proximity share, that per-frame close-proximity event counts vary approximately 10-fold across the eight unique locations at three intersections, and that 83% of pedestrian bounding boxes are small in image space, indicating that VRUs are typically far from any single sensor. These findings support multi-modal sensing, edge-side analytics, and adaptive context-sensitive deployment rather than uniform single-sensor solutions.

翻译：弱势道路使用者（VRUs）约占全球城市交通死亡人数的一半，而交叉口集中了其中不成比例的伤亡案例。近期关于VRU保护的感知技术综述已列举数十种单传感器与双传感器部署方案，然而这些系统均未能在单个交叉口机柜中实现多模态感知、边缘端近碰撞分析以及双向车联万物（V2X）与行人联万物（P2X）消息传递的耦合。本文提出一种面向信号交叉口VRU保护的一体化框架，在感知层融合LiDAR、雷达、RGB相机与热成像相机，在计算层集成边缘端预测与替代安全分析，在通信层集成V2X与P2X消息传递，并在执行层集成自适应信号控制。该框架基于实证案例研究，采用R-LiViT——首个公开的路侧LiDAR-视觉-热成像数据集，该数据集包含200个多模态序列及来自德国三个交叉口的2,400个标注RGB-T帧。对53,319个检测标注的分析表明：VRU约占所有道路使用者观测量的49%；行人密度昼夜下降38%，车辆下降45%，而夜间分布中近距离占比更高；三个交叉口八个不同位置的单帧近距离事件计数变化约10倍；83%的行人边界框在图像空间中为小目标，表明VRU通常远离任何单一传感器。这些发现支持多模态感知、边缘端分析以及自适应情境感知部署方案，而非统一的单传感器解决方案。