# Vulnerable Road User Detection for Roadside-Assisted Safety Protection: A Comprehensive Survey
## 3 VRU Detection Models
### Multi-Sensor
#### 3.2.1 Classification by Modality
Then, a real-time adaptive foreground extractor was proposed using the Mixture of Gaussian(MoG) [114], and then an improved DBSCAN [112] was achieved by projecting the points to a 2D plane and dividing the clustering area with cells to reduce computation. Limited by the fixed range and the required minimum number of clusters in DBSCAN, when it comes to two pedestrians being very close, it is very challenging to separate them. In InfraDet3D [111], different fusion methods of two LiDARs were proposed as the baseline methods, including early fusion and late fusion. Early fusion of a LiDAR-only setup is to merge numerous point cloud scans generated by various LiDAR sensors at time step \(t\) to create a unified and densely populated point cloud, which used Fast Global Registration [115] to register the merged point cloud in the initial transformation and point-to-point Iterative Closest Point (ICP) [116] for the refinement. A late fusion baseline method of LiDAR was also talked about in the paper, which continued to use the same late fusion approach mentioned in camera-only fusion. In this work, pedestrians and cyclists were considered in the detection but not as the main objects. Although not much attention is given to the VRUs, the two fusion methods proposed are very straightforward and computationally cheap, which gives some inspiration for roadside real-time VRU sensing. Additionally, feature-level fusion using multiple LiDARs has been explored. For example, PillarGrid [117] extracts features from two different LiDARs in a pillar-wise manner and fuses them in a grid-wise fashion to enhance detection performance and extend the detection range. * Multi-Modal Regarding multi-modal sensor fusion, various sensor combinations can be used for detection, including but not limited to cameras and LiDAR or cameras and RADAR. However, this paper primarily focuses on these two common combinations for VRU detection, with other multi-modal fusion methods briefly introduced. Camera and LiDAR: The fusion of camera and LiDAR sensors is one of the most common multi-modal sensor combinations. Cameras provide rich features that are beneficial for classification and detection during the daytime, while LiDAR can detect objects at night and offer more accurate point cloud data without distortion. In recent years, many promising fusion methods using cameras and LiDAR have been proposed. Generic fusion methods are listed in Table 4, with fusion strategies categorized based on pairs of sensor-derived feature types. Each category combines specific types of features from LiDAR and camera sensors; for example, FV/BEV and feature map represent features derived from LiDAR's FV/BEV and camera's feature map. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category Based on Feature Extraction Method** & **Methods** & **Comments** \\ \hline  & & \
\begin{tabular}{p{113.8 pt}} BV provides spatial context for VRUs, and camera feature maps \\ enhance object classification; FV may not be as robust as BEV for VRU detection, especially for detecting VRUs that might be occluded or outside the direct front-view. \\ Voxel and feature map-based & MVXNet [55], 3D-CVF [56], VPF-Net [57], PointAugmenting [58] \\ \end{tabular} &
\begin{tabular}{p{113.8 pt}} BV provides spatial context for VRUs, and camera feature maps \\ enhance object classification; FV may not be as robust as BEV for VRU detection, especially for detecting VRUs that might be occluded or outside the direct front-view. \\ Voxel grid provides depth but lacks high resolution for fine VRU detection. Combined with feature maps, detection improves but may struggle in cluttered environments. \\ \end{tabular}
\end{table} 
Table 4: Comparison of different generic camera and LiDAR fusion methods by category. In roadside sensor fusion applications, methods have evolved from assigning distinct functions to individual sensors for detection to integrating detections or features from multiple sensors. [PERSON] et al.