# Vulnerable Road User Detection for Roadside-Assisted Safety Protection: A Comprehensive Survey
## 3 VRU Detection Models  
In the traffic domain, with the progress of autonomous driving, an increasing number of algorithms and datasets for vehicle detection have been proposed, and vehicle detection accuracy has become increasingly accurate. In comparison, VRU detection has received less attention from scholars. Some generic detection algorithms include pedestrians and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensors** & **Advantages** & **Disadvantages** & **Cost** & **Durability** \\ \hline \multirow{8}{*}{Monocular Camera} & \multirow{8}{*}{Rich Information (Signs and Objects)} & Strong Light & & \\  & & Dependence; Poor & & \\  & & Environmental & & \\  & & Adaptability; Low & & \\  & & Ranging Accuracy; & & \\  & & Low Privacy; & & \\  & & Narrow Field of & & \\  & & View (FOV); Limited & & \\  & & 3D Information & & \\ \hline \multirow{8}{*}{LiDAR} & Simple Structure; & & & \\  & Low Power & & & \\  & Consumption; & & & \\  & Strong & & & \\  & Environmental & & Read Signs and & \\  & Adaptability; & & Distinguish Colors & & \\  & Extensive FOV; High & & & \\  & Accuracy; High & & & \\  & Privacy; Rich 3D & & & \\  & Information & & & \\ \hline \multirow{8}{*}{RADAR} & Long Detection & & & \\  & Distance; High & & & \\  & Accuracy in Object & & & \\  & Position and Speed; & & & \\  & Strong & & & \\  & Environmental & & & \\  & Adaptability; High & & & \\  & Privacy & & & \\ \hline \multirow{8}{*}{Depth Camera} & Accurate Depth & & & \\  & Perception; & & & \\  & Moderate Light & & & \\  & Dependence & & & \\  & Dependence & & & \\ \hline \multirow{8}{*}{Infrared Camera (IR)} & Low Light & Narrow FOV; & & \\  & Dependence; Strong & & & \\  & Environmental & & & \\  & Adaptability & & & \\ \hline \multirow{8}{*}{Fisherye Camera} & Extensive FOV; Rich & & & \\  & Information (Signs & & \\  & and Objects) & & & \\ \hline \multirow{8}{*}{Event-based Camera} & Robustness to & & & \\  & Motion Blur; Low & & & \\ \cline{1-1}  & Power Consumption; & & & \\ \cline{1-1}  & High Dynamic & & & \\ \cline{1-1}  & Range; Minimal Data & & & \\ \cline{1-1}  & Rate & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection Sensors. bicyclists in the categories of recognized objects, but their recognition rates are generally low. For example, VoxelNet's [8] detection performances in car, pedestrian, and cyclist are 81.97%, 57.86%, and 67.17% in average precision on the easy KITTI [9] validation set. At the same time, research focusing on VRU detection models and datasets has not made systematic progress, which makes the summary of the relevant literature on VRU detection even more important. The papers on the detection models we reviewed are all related to VRU detection. Some are generic detection models with other VRUs in the categories of recognized objects, and some are specifically focusing on VRU detection. The ratio of detection models we reviewed belonging to the two categories is shown in Figure 2. Specifically, the VRU detection models are developed for various explicit VRUs, including pedestrians, cyclists, skateboarders, e-scooter users, wheelchair users, stroller users, and even pedestrians using umbrellas, which are scattered and not systematic [10; 11; 12; 13; 14; 15; 16]. In general, the VRU detection models utilized rather conventional or simple methods implemented on a specific VRU category without progressive progress. VRUs are generally small in size, have inconsistent speed distribution, and are very dynamic. The inconsistent speed distribution means similar-sized scooters, bicycles, and skateboards move rather fast; however, pedestrians and wheelchairs move slower. The elderly, especially, move much more slowly. Dynamic changes mean that VRUs do not necessarily travel strictly in prescribed lane areas or along smooth predictable trajectories; they may rush out of blind spots at any time, and children may run across pedestrian areas.