# Vulnerable Road User Detection for Roadside-Assisted Safety Protection: A Comprehensive Survey
## 3 VRU Detection Models
### Single-Sensor
#### 3.1.2 LiDAR-Based Detection
Voxel-based methods divide the 3D space into a structured grid of voxels and use CNNs to extract features, as seen in VoxelNet [8] and SECOND [40]. A variant, pillar-based methods, segment point clouds into pillars by extending voxel boundaries in the z-direction, as demonstrated in PointPillars [41]. Point-based methods directly process raw, unstructured 3D point clouds, using networks like PointNet [101] or PointRCNN [44] to capture fine-grained local and global features. For VRU detection, different feature-extracting categories have their characteristics, which are analyzed in Table 3 to show their pros and cons for VRU detection. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category Based on** & & & \\
**Feature Extraction** & **Methods** & **Pros** & **Cons** \\ Method & & & \\ \hline \hline \multirow{4}{*}{\
\begin{tabular}{c} Front view \\ (FV)-based \\ \end{tabular} } & \multirow{4}{*}{VeloFCN [38]} & Good for detecting & \multirow{4}{*}{
\begin{tabular}{c} Struggles with \\ occlusions and side \\ or distant VRUs. \\ \end{tabular} } \\  & & VeloFCN [38] & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \end{tabular}
\end{table} 
Table 3: Comparison of different LiDAR detection methods by category. Specifically, in DL-based roadside VRU detection methods using a single LiDAR sensor, researchers have primarily enhanced conventional approaches by replacing one of the modules with a deep learning model, while maintaining the original pipeline. In the early stages, researchers simply replaced the final classification step with DL and attempted to transform the data and features from 3D to 2D. For example, in [103], after background filtering, clustering, and feature extraction, a lightweight Visual Geometry Group Network (VGGNet) was used to classify objects with hand-crafted features as inputs. With the VGGNet and a rich set of features, the model outperformed the Random Forest (RF) by 14.4% and 11.9% in detecting pedestrians and cyclists, respectively, and surpassed the Support Vector Machine (SVM) by 10.4% and 14.3% in the same categories. Similarly, [PERSON] et al. [104] proposed a Global Context Network (GCNet) prototype, consisting of three stages: gridding, clustering, and classification. In the gridding stage, the LiDAR point clouds were transformed into a grid format in a plane, enabling the use of a CNN for classifying clustered objects, much like image classification in the 2D plane. Recently, researchers have shifted towards using end-to-end deep learning networks for roadside VRU detection with LiDAR, replacing traditional clustering, feature extraction, and classification steps. Some studies focused on adapting mature models developed for onboard applications to roadside scenarios. [PERSON] et al. [105] analyzed key milestones of LiDAR-based detection for onboard systems and compared their performance in roadside surveillance, including SECOND [40], CenterPoint [42] with voxel encoding, CenterPoint with pillar encoding, and PillarNet [43]. Compared to anchor-based methods, center-based methods showed better performance with lower computational demands. Among the methods tested, CenterPoint using voxel representation with specific hyperparameters (distinct from onboard setups) achieved the best performance for pedestrian detection in video surveillance scenarios. Meanwhile, fewer researchers have focused on developing dedicated roadside 3D object detection models, including for VRU objects. Taking the Feature Enhancement Network (FecNet) as an example, FecNet [102] is a pillar-based feature enhancement cascade network designed for roadside object detection with LiDAR.