# Vulnerable Road User Detection for Roadside-Assisted Safety Protection: A Comprehensive Survey
## 3 VRU Detection Models
### Multi-Sensor
#### 3.2.1 Classification by Modality  
With the development of the perception research domain, multiple sensors have been utilized to detect the VRUs in traffic systems. In the early stage, some researchers utilized the combination of three low-cost sensors, i.e., Doppler microwave RADAR sensors, passive infrared (PIR), and geomagnetic sensors at the roadside to perceive if there is a pedestrian or vehicle passing [14]. With the cost of sensors going down, increasing numbers of advanced accurate sensors are being utilized at intersections. Different sensors have different advantages in detecting VRUs, which is explained in Table 1. Cameras can provide texture and color, but their detection results are significantly affected by the weather and illumination. RADAR is suitable for long-range detection and operates effectively in all weather conditions but has low resolution. LiDARs can provide depth information under various environmental conditions but with a lower density of detail than camera imagery. Infrared cameras can be effectively used to detect VRUs but can be affected by temperature. Thus, multiple sensors should be combined to detect objects cooperatively, which will fully tap the potentials of different sensors. The detection methods based on sensor modality can be classified into single-modal and multi-modal. For single-modal methods, we focus on the camera-only and the LiDAR-only methods, while for multi-modal approaches, combining the camera with LiDAR or combining the camera with RADAR are prevalent setups. * Single-Modal Camera only: Multiple cameras can provide a wider field of view (FOV) and more accurate results in VRU detection, particularly in addressing issues related to illumination and occlusion. Inspired by research in the multiple-camera multi-object tracking (MOT) domain [110], cameras from different directions can be fused to detect and track objects. In this approach, objects are first detected in each camera view, and then their movement is tracked across multiple cameras, which constitutes the fusion in the temporal aspect. This paper, however, focuses only on the detection aspect, meaning that the primary goal of using multiple cameras from different directions is to solve issues of illumination and occlusion to ensure VRUs are detected, rather than performing re-identification and tracking. In this context, detections from different cameras are used to complement one another. Moreover, camera fusion can enhance the detection performance. For example, in InfraDet3D [111], a fusion method for the 3D detection results from two roadside cameras was proposed as one of the baseline approaches. In this method, the detections from two cameras are transformed to the same reference frame and matched within a threshold distance. The merging process involved combining matched detections by choosing the central position and yaw vector of the identified object from the nearest sensor. The dimensions of the merged detections were determined by averaging the measurements from both detectors. In general, camera-only fusion with a post-processing technique is designed to complement the detection results from different cameras and improve the detection accuracy of objects detected simultaneously, thus enhancing the safety of VRUs with greater confidence. In recent years, deep learning-based feature fusion methods for multi-view 3D object detection, such as DET83D [32] and PETR [33], have been proposed using onboard-collected images. However, their potential performance in roadside detection remains underexplored. LiDAR only: Multiple LiDARs can improve detection accuracy by registering data from different viewpoints to obtain a higher-quality point cloud or by combining detections through late fusion. [PERSON] et al. [112] established a real-time pedestrian detection network of eight multilayer laser scanners in the KoPER project [113]. The collected data from different sensors are simply registered together.